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Program Manager

Discussion

Objectives of the Role:

We are seeking a seasoned AI Delivery Manager (AVP level) to lead end-to-end delivery of AI programs—including GenAI and classical ML—across diverse technology stacks. This role owns delivery outcomes from discovery through production, driving measurable business value, robust governance, and adoption across data, model, application, and platform workstreams.

The ideal candidate will lead multiple high-impact AI initiatives, manage cross-functional teams (data science, data engineering, product, platform, security, and QA), and ensure delivery predictability, compliance, and adoption in Agile/Hybrid environments. Responsibilities

1. Program & Project Leadership (End-to-End Ownership)

  • Lead end-to-end delivery for multiple AI initiatives (discovery, solution design, build, validation, deployment, and hypercare) across varied tech stacks.
  • Own delivery accountability: scope, schedule, budget, quality, risk, and realized business outcomes (adoption, accuracy, productivity, cost, revenue).
  • Establish integrated plans across data engineering, model development, application engineering, and platform/MLOps to ensure predictable releases and production stability.

2. Strategic Planning & Governance

  • Establish program governance including AI-specific KPIs/OKRs (model performance, drift, latency, cost-to-serve), release gates, and audit-ready documentation.
  • Oversee estimation, staffing, and capacity planning (data, DS/ML, prompt/LLM engineering, backend/frontend, cloud, security) and manage dependencies across teams.
  • Ensure compliance with SDLC/MLLC, data governance, privacy, security, and model risk standards; drive proactive RAID management and control effectiveness.

3. AI/ML Delivery Oversight (Critical Capability)

  • Lead delivery for AI/ML and GenAI solutions (including RAG, copilots, and intelligent automation), ensuring fit-for-purpose architecture and alignment to business objectives.
  • Guide teams across the AI lifecycle: use-case framing, data readiness, experimentation, evaluation, fine-tuning, deployment, observability (drift/quality), and continuous improvement.
  • Identify and mitigate risks related to data quality, bias/fairness, IP/privacy, security, hallucinations, model performance, scalability, cost, and user adoption; ensure responsible AI practices.
  • Partner with platform teams to implement MLOps/LLMOps (CI/CD, model registry, evaluation harnesses, prompt/version control, guardrails, monitoring, and incident management).

4. Stakeholder & Executive Management

  • Engage executive stakeholders to define outcomes, success metrics, and decision cadence; ensure alignment across business, risk, compliance, and technology leaders.
  • Provide crisp, decision-oriented reporting on delivery health, model/product KPIs, risks, and value realization; tailor communication for technical and non-technical audiences.
  • Manage escalations (delivery, security, model quality, production incidents, vendor dependencies) and ensure timely, root-cause-driven resolution.

5. Agile & Hybrid Delivery Leadership

  • Select and scale Agile/Waterfall/Hybrid delivery models appropriate for AI work (experimentation + productization), balancing speed with controls.
  • Ensure effective Agile rituals and artifact quality (roadmaps, backlogs, user stories, acceptance criteria, experiment logs) with alignment to program milestones.
  • Drive delivery predictability, quality, and throughput improvements using metrics (velocity, lead time, defect leakage, model KPI trends) and retrospectives.

6. Cross-Functional & Vendor Management

  • Lead collaboration across data engineering, data science/ML, application engineering, product, design, QA, cloud/platform, and InfoSec to deliver cohesive AI products.
  • Manage vendors and partners (including model/API providers) across scope, SLAs, cost, security reviews, and delivery milestones; ensure contractual and compliance alignment.

7. Financial & Resource Management

  • Own budgeting, forecasting, and cost control; manage AI-specific cost drivers (training/inference, vector storage, data processing, cloud consumption, licensing).
  • Optimize resource allocation and delivery plans to maximize throughput, reduce cycle time, and improve cost-to-value across AI programs.

8. Risk, Compliance & Quality Assurance

  • Run proactive risk management (RAID) including AI-specific risks; define mitigations, owners, and control checkpoints throughout the lifecycle.
  • Ensure adherence to compliance, data governance, and quality standards; implement robust AI testing/evaluation (offline metrics, red-teaming, prompt regression, bias checks) before release.

9. Continuous Improvement & Delivery Excellence

  • Institutionalize best practices (delivery playbooks, reusable templates, evaluation checklists) and run lessons-learned to continuously raise delivery maturity.
  • Coach project/program managers and engineering leaders on AI delivery, governance, and operating rhythms to build a high-performing, accountable culture.

Skills
Technical Skills
  • Strong understanding of GenAI/LLM concepts (prompting, embeddings, vector databases, RAG, tool/function calling) and enterprise-grade AI solution patterns.
  • Strong command of the AI/ML lifecycle and delivery artifacts: problem framing, experimentation, evaluation, deployment, monitoring, retraining, and decommissioning.
  • Experience delivering AI use cases such as conversational AI, document intelligence, search/knowledge assistants, personalization/recommendations, forecasting, and enterprise automation.
  • Working knowledge of MLOps/LLMOps: CI/CD, model registry, experiment tracking, evaluation harnesses, prompt/version control, monitoring/alerting, and incident response.
  • Hands-on experience with delivery toolchains (JIRA/Azure DevOps, Confluence, MS Project) and engineering practices (Git, CI/CD, release management) for tracking and governance.
  • Working knowledge of cloud platforms (AWS/Azure/GCP) and their AI & data ecosystems (managed ML services, GPU infrastructure, container platforms, managed databases, secret management).
  • Exposure to intelligent automation and workflow orchestration; ability to integrate AI into business processes and enterprise applications and managing stakeholder expectations with AI Products/Projects.
  • Proven expertise in end-to-end delivery for AI-enabled products (initiation through production support), with strong focus on adoption, reliability, and continuous improvement.
  • Strong understanding of SDLC/MLLC, architecture governance, testing strategies, and production operations for AI systems.
  • Deep expertise in Agile (Scrum/Kanban), Waterfall, and hybrid models—able to run experimentation cycles and transition to scalable product delivery.
  • Advanced capabilities in project planning, scheduling, resource allocation, budgeting, and cost control.
  • Strong competency in risk assessment, issue management, dependency tracking, and scope governance in complex delivery environments.
  • Ability to adapt project priorities, timelines, and scope in response to evolving business and technology needs.
  • Proven experience in executive-level reporting, stakeholder communication, and governance reviews.
Soft skills (Desired)
  • Excellent communication, executive presence, and interpersonal skills, with the ability to engage senior stakeholders, manage client relationships, and translate complex (including AI-driven) concepts into clear business outcomes.
  • Strong analytical and strategic thinking, with attention to detail in tracking deliverables, KPIs, risks, and program milestones, enabling data-driven decision-making.
  • Proven ability to collaborate across cross-functional teams, influence senior stakeholders, and drive alignment in complex, matrixed environments.
  • Demonstrated capability to lead, mentor, and build high-performing delivery teams, fostering accountability, ownership, and continuous improvement.
  • Strong problem-solving and decision-making skills in high-pressure, fast-paced delivery environments.
Education Requirements
  • Graduate
  • PMP/Prince2 Mandatory
  • Agile certifications (CSM / SAFe Agilist or equivalent) strongly preferred / mandatory.
  • GENAI certification – CPMAI etc preferred

Work Experience Requirements:
Must Have:

  • 10–12 years of overall experience, with at least 8+ years in project/program management within IT or digital transformation environments.
  • Proven experience managing multiple concurrent projects/programs (minimum two in parallel).
  • Demonstrated experience delivering projects using both:
    • Agile methodologies (Scrum Master / Agile PM role)
    • Waterfall model (end-to-end Project Manager ownership)
  • Strong track record of end-to-end project delivery (initiation to closure, including post-production support).
  • Must have Hands-on experience managing at least 1 AI/ML project (including GenAI/LLM-based initiatives is highly desirable).
  • Experience in client-facing or consulting environments, with strong stakeholder and expectation management.

  • Proven ability to manage scope, timelines, budgets, risks, and cross-functional teams in complex delivery environments.

Preferred Skills:

  • Experience with low-code/no-code platforms or enterprise business system implementations.
  • Exposure to GenAI / LLM-based solutions, automation, or advanced analytics use cases.
  • Relevant certifications such as PMP, PRINCE2, SAFe, Certified Scrum Master (CSM), or AI/GenAI certifications (e.g., CPMAI or equivalent).

Objectives of the Role:

We are seeking a seasoned AI Delivery Manager (AVP level) to lead end-to-end delivery of AI programs—including GenAI and classical ML—across diverse technology stacks. This role owns delivery outcomes from discovery through production, driving measurable business value, robust governance, and adoption across data, model, application, and platform workstreams.

The ideal candidate will lead multiple high-impact AI initiatives, manage cross-functional teams (data science, data engineering, product, platform, security, and QA), and ensure delivery predictability, compliance, and adoption in Agile/Hybrid environments. Responsibilities

1. Program & Project Leadership (End-to-End Ownership)

  • Lead end-to-end delivery for multiple AI initiatives (discovery, solution design, build, validation, deployment, and hypercare) across varied tech stacks.
  • Own delivery accountability: scope, schedule, budget, quality, risk, and realized business outcomes (adoption, accuracy, productivity, cost, revenue).
  • Establish integrated plans across data engineering, model development, application engineering, and platform/MLOps to ensure predictable releases and production stability.

2. Strategic Planning & Governance

  • Establish program governance including AI-specific KPIs/OKRs (model performance, drift, latency, cost-to-serve), release gates, and audit-ready documentation.
  • Oversee estimation, staffing, and capacity planning (data, DS/ML, prompt/LLM engineering, backend/frontend, cloud, security) and manage dependencies across teams.
  • Ensure compliance with SDLC/MLLC, data governance, privacy, security, and model risk standards; drive proactive RAID management and control effectiveness.

3. AI/ML Delivery Oversight (Critical Capability)

  • Lead delivery for AI/ML and GenAI solutions (including RAG, copilots, and intelligent automation), ensuring fit-for-purpose architecture and alignment to business objectives.
  • Guide teams across the AI lifecycle: use-case framing, data readiness, experimentation, evaluation, fine-tuning, deployment, observability (drift/quality), and continuous improvement.
  • Identify and mitigate risks related to data quality, bias/fairness, IP/privacy, security, hallucinations, model performance, scalability, cost, and user adoption; ensure responsible AI practices.
  • Partner with platform teams to implement MLOps/LLMOps (CI/CD, model registry, evaluation harnesses, prompt/version control, guardrails, monitoring, and incident management).

4. Stakeholder & Executive Management

  • Engage executive stakeholders to define outcomes, success metrics, and decision cadence; ensure alignment across business, risk, compliance, and technology leaders.
  • Provide crisp, decision-oriented reporting on delivery health, model/product KPIs, risks, and value realization; tailor communication for technical and non-technical audiences.
  • Manage escalations (delivery, security, model quality, production incidents, vendor dependencies) and ensure timely, root-cause-driven resolution.

5. Agile & Hybrid Delivery Leadership

  • Select and scale Agile/Waterfall/Hybrid delivery models appropriate for AI work (experimentation + productization), balancing speed with controls.
  • Ensure effective Agile rituals and artifact quality (roadmaps, backlogs, user stories, acceptance criteria, experiment logs) with alignment to program milestones.
  • Drive delivery predictability, quality, and throughput improvements using metrics (velocity, lead time, defect leakage, model KPI trends) and retrospectives.

6. Cross-Functional & Vendor Management

  • Lead collaboration across data engineering, data science/ML, application engineering, product, design, QA, cloud/platform, and InfoSec to deliver cohesive AI products.
  • Manage vendors and partners (including model/API providers) across scope, SLAs, cost, security reviews, and delivery milestones; ensure contractual and compliance alignment.

7. Financial & Resource Management

  • Own budgeting, forecasting, and cost control; manage AI-specific cost drivers (training/inference, vector storage, data processing, cloud consumption, licensing).
  • Optimize resource allocation and delivery plans to maximize throughput, reduce cycle time, and improve cost-to-value across AI programs.

8. Risk, Compliance & Quality Assurance

  • Run proactive risk management (RAID) including AI-specific risks; define mitigations, owners, and control checkpoints throughout the lifecycle.
  • Ensure adherence to compliance, data governance, and quality standards; implement robust AI testing/evaluation (offline metrics, red-teaming, prompt regression, bias checks) before release.

9. Continuous Improvement & Delivery Excellence

  • Institutionalize best practices (delivery playbooks, reusable templates, evaluation checklists) and run lessons-learned to continuously raise delivery maturity.
  • Coach project/program managers and engineering leaders on AI delivery, governance, and operating rhythms to build a high-performing, accountable culture.

Skills
Technical Skills
  • Strong understanding of GenAI/LLM concepts (prompting, embeddings, vector databases, RAG, tool/function calling) and enterprise-grade AI solution patterns.
  • Strong command of the AI/ML lifecycle and delivery artifacts: problem framing, experimentation, evaluation, deployment, monitoring, retraining, and decommissioning.
  • Experience delivering AI use cases such as conversational AI, document intelligence, search/knowledge assistants, personalization/recommendations, forecasting, and enterprise automation.
  • Working knowledge of MLOps/LLMOps: CI/CD, model registry, experiment tracking, evaluation harnesses, prompt/version control, monitoring/alerting, and incident response.
  • Hands-on experience with delivery toolchains (JIRA/Azure DevOps, Confluence, MS Project) and engineering practices (Git, CI/CD, release management) for tracking and governance.
  • Working knowledge of cloud platforms (AWS/Azure/GCP) and their AI & data ecosystems (managed ML services, GPU infrastructure, container platforms, managed databases, secret management).
  • Exposure to intelligent automation and workflow orchestration; ability to integrate AI into business processes and enterprise applications and managing stakeholder expectations with AI Products/Projects.
  • Proven expertise in end-to-end delivery for AI-enabled products (initiation through production support), with strong focus on adoption, reliability, and continuous improvement.
  • Strong understanding of SDLC/MLLC, architecture governance, testing strategies, and production operations for AI systems.
  • Deep expertise in Agile (Scrum/Kanban), Waterfall, and hybrid models—able to run experimentation cycles and transition to scalable product delivery.
  • Advanced capabilities in project planning, scheduling, resource allocation, budgeting, and cost control.
  • Strong competency in risk assessment, issue management, dependency tracking, and scope governance in complex delivery environments.
  • Ability to adapt project priorities, timelines, and scope in response to evolving business and technology needs.
  • Proven experience in executive-level reporting, stakeholder communication, and governance reviews.
Soft skills (Desired)
  • Excellent communication, executive presence, and interpersonal skills, with the ability to engage senior stakeholders, manage client relationships, and translate complex (including AI-driven) concepts into clear business outcomes.
  • Strong analytical and strategic thinking, with attention to detail in tracking deliverables, KPIs, risks, and program milestones, enabling data-driven decision-making.
  • Proven ability to collaborate across cross-functional teams, influence senior stakeholders, and drive alignment in complex, matrixed environments.
  • Demonstrated capability to lead, mentor, and build high-performing delivery teams, fostering accountability, ownership, and continuous improvement.
  • Strong problem-solving and decision-making skills in high-pressure, fast-paced delivery environments.
Education Requirements
  • Graduate
  • PMP/Prince2 Mandatory
  • Agile certifications (CSM / SAFe Agilist or equivalent) strongly preferred / mandatory.
  • GENAI certification – CPMAI etc preferred

Work Experience Requirements:
Must Have:

  • 10–12 years of overall experience, with at least 8+ years in project/program management within IT or digital transformation environments.
  • Proven experience managing multiple concurrent projects/programs (minimum two in parallel).
  • Demonstrated experience delivering projects using both:
    • Agile methodologies (Scrum Master / Agile PM role)
    • Waterfall model (end-to-end Project Manager ownership)
  • Strong track record of end-to-end project delivery (initiation to closure, including post-production support).
  • Must have Hands-on experience managing at least 1 AI/ML project (including GenAI/LLM-based initiatives is highly desirable).
  • Experience in client-facing or consulting environments, with strong stakeholder and expectation management.

  • Proven ability to manage scope, timelines, budgets, risks, and cross-functional teams in complex delivery environments.

Preferred Skills:

  • Experience with low-code/no-code platforms or enterprise business system implementations.
  • Exposure to GenAI / LLM-based solutions, automation, or advanced analytics use cases.
  • Relevant certifications such as PMP, PRINCE2, SAFe, Certified Scrum Master (CSM), or AI/GenAI certifications (e.g., CPMAI or equivalent).

Objectives of the Role:

We are seeking a seasoned AI Delivery Manager (AVP level) to lead end-to-end delivery of AI programs—including GenAI and classical ML—across diverse technology stacks. This role owns delivery outcomes from discovery through production, driving measurable business value, robust governance, and adoption across data, model, application, and platform workstreams.

The ideal candidate will lead multiple high-impact AI initiatives, manage cross-functional teams (data science, data engineering, product, platform, security, and QA), and ensure delivery predictability, compliance, and adoption in Agile/Hybrid environments. Responsibilities

1. Program & Project Leadership (End-to-End Ownership)

  • Lead end-to-end delivery for multiple AI initiatives (discovery, solution design, build, validation, deployment, and hypercare) across varied tech stacks.
  • Own delivery accountability: scope, schedule, budget, quality, risk, and realized business outcomes (adoption, accuracy, productivity, cost, revenue).
  • Establish integrated plans across data engineering, model development, application engineering, and platform/MLOps to ensure predictable releases and production stability.

2. Strategic Planning & Governance

  • Establish program governance including AI-specific KPIs/OKRs (model performance, drift, latency, cost-to-serve), release gates, and audit-ready documentation.
  • Oversee estimation, staffing, and capacity planning (data, DS/ML, prompt/LLM engineering, backend/frontend, cloud, security) and manage dependencies across teams.
  • Ensure compliance with SDLC/MLLC, data governance, privacy, security, and model risk standards; drive proactive RAID management and control effectiveness.

3. AI/ML Delivery Oversight (Critical Capability)

  • Lead delivery for AI/ML and GenAI solutions (including RAG, copilots, and intelligent automation), ensuring fit-for-purpose architecture and alignment to business objectives.
  • Guide teams across the AI lifecycle: use-case framing, data readiness, experimentation, evaluation, fine-tuning, deployment, observability (drift/quality), and continuous improvement.
  • Identify and mitigate risks related to data quality, bias/fairness, IP/privacy, security, hallucinations, model performance, scalability, cost, and user adoption; ensure responsible AI practices.
  • Partner with platform teams to implement MLOps/LLMOps (CI/CD, model registry, evaluation harnesses, prompt/version control, guardrails, monitoring, and incident management).

4. Stakeholder & Executive Management

  • Engage executive stakeholders to define outcomes, success metrics, and decision cadence; ensure alignment across business, risk, compliance, and technology leaders.
  • Provide crisp, decision-oriented reporting on delivery health, model/product KPIs, risks, and value realization; tailor communication for technical and non-technical audiences.
  • Manage escalations (delivery, security, model quality, production incidents, vendor dependencies) and ensure timely, root-cause-driven resolution.

5. Agile & Hybrid Delivery Leadership

  • Select and scale Agile/Waterfall/Hybrid delivery models appropriate for AI work (experimentation + productization), balancing speed with controls.
  • Ensure effective Agile rituals and artifact quality (roadmaps, backlogs, user stories, acceptance criteria, experiment logs) with alignment to program milestones.
  • Drive delivery predictability, quality, and throughput improvements using metrics (velocity, lead time, defect leakage, model KPI trends) and retrospectives.

6. Cross-Functional & Vendor Management

  • Lead collaboration across data engineering, data science/ML, application engineering, product, design, QA, cloud/platform, and InfoSec to deliver cohesive AI products.
  • Manage vendors and partners (including model/API providers) across scope, SLAs, cost, security reviews, and delivery milestones; ensure contractual and compliance alignment.

7. Financial & Resource Management

  • Own budgeting, forecasting, and cost control; manage AI-specific cost drivers (training/inference, vector storage, data processing, cloud consumption, licensing).
  • Optimize resource allocation and delivery plans to maximize throughput, reduce cycle time, and improve cost-to-value across AI programs.

8. Risk, Compliance & Quality Assurance

  • Run proactive risk management (RAID) including AI-specific risks; define mitigations, owners, and control checkpoints throughout the lifecycle.
  • Ensure adherence to compliance, data governance, and quality standards; implement robust AI testing/evaluation (offline metrics, red-teaming, prompt regression, bias checks) before release.

9. Continuous Improvement & Delivery Excellence

  • Institutionalize best practices (delivery playbooks, reusable templates, evaluation checklists) and run lessons-learned to continuously raise delivery maturity.
  • Coach project/program managers and engineering leaders on AI delivery, governance, and operating rhythms to build a high-performing, accountable culture.

Skills
Technical Skills
  • Strong understanding of GenAI/LLM concepts (prompting, embeddings, vector databases, RAG, tool/function calling) and enterprise-grade AI solution patterns.
  • Strong command of the AI/ML lifecycle and delivery artifacts: problem framing, experimentation, evaluation, deployment, monitoring, retraining, and decommissioning.
  • Experience delivering AI use cases such as conversational AI, document intelligence, search/knowledge assistants, personalization/recommendations, forecasting, and enterprise automation.
  • Working knowledge of MLOps/LLMOps: CI/CD, model registry, experiment tracking, evaluation harnesses, prompt/version control, monitoring/alerting, and incident response.
  • Hands-on experience with delivery toolchains (JIRA/Azure DevOps, Confluence, MS Project) and engineering practices (Git, CI/CD, release management) for tracking and governance.
  • Working knowledge of cloud platforms (AWS/Azure/GCP) and their AI & data ecosystems (managed ML services, GPU infrastructure, container platforms, managed databases, secret management).
  • Exposure to intelligent automation and workflow orchestration; ability to integrate AI into business processes and enterprise applications and managing stakeholder expectations with AI Products/Projects.
  • Proven expertise in end-to-end delivery for AI-enabled products (initiation through production support), with strong focus on adoption, reliability, and continuous improvement.
  • Strong understanding of SDLC/MLLC, architecture governance, testing strategies, and production operations for AI systems.
  • Deep expertise in Agile (Scrum/Kanban), Waterfall, and hybrid models—able to run experimentation cycles and transition to scalable product delivery.
  • Advanced capabilities in project planning, scheduling, resource allocation, budgeting, and cost control.
  • Strong competency in risk assessment, issue management, dependency tracking, and scope governance in complex delivery environments.
  • Ability to adapt project priorities, timelines, and scope in response to evolving business and technology needs.
  • Proven experience in executive-level reporting, stakeholder communication, and governance reviews.
Soft skills (Desired)
  • Excellent communication, executive presence, and interpersonal skills, with the ability to engage senior stakeholders, manage client relationships, and translate complex (including AI-driven) concepts into clear business outcomes.
  • Strong analytical and strategic thinking, with attention to detail in tracking deliverables, KPIs, risks, and program milestones, enabling data-driven decision-making.
  • Proven ability to collaborate across cross-functional teams, influence senior stakeholders, and drive alignment in complex, matrixed environments.
  • Demonstrated capability to lead, mentor, and build high-performing delivery teams, fostering accountability, ownership, and continuous improvement.
  • Strong problem-solving and decision-making skills in high-pressure, fast-paced delivery environments.
Education Requirements
  • Graduate
  • PMP/Prince2 Mandatory
  • Agile certifications (CSM / SAFe Agilist or equivalent) strongly preferred / mandatory.
  • GENAI certification – CPMAI etc preferred

Work Experience Requirements:
Must Have:

  • 10–12 years of overall experience, with at least 8+ years in project/program management within IT or digital transformation environments.
  • Proven experience managing multiple concurrent projects/programs (minimum two in parallel).
  • Demonstrated experience delivering projects using both:
    • Agile methodologies (Scrum Master / Agile PM role)
    • Waterfall model (end-to-end Project Manager ownership)
  • Strong track record of end-to-end project delivery (initiation to closure, including post-production support).
  • Must have Hands-on experience managing at least 1 AI/ML project (including GenAI/LLM-based initiatives is highly desirable).
  • Experience in client-facing or consulting environments, with strong stakeholder and expectation management.

  • Proven ability to manage scope, timelines, budgets, risks, and cross-functional teams in complex delivery environments.

Preferred Skills:

  • Experience with low-code/no-code platforms or enterprise business system implementations.
  • Exposure to GenAI / LLM-based solutions, automation, or advanced analytics use cases.
  • Relevant certifications such as PMP, PRINCE2, SAFe, Certified Scrum Master (CSM), or AI/GenAI certifications (e.g., CPMAI or equivalent).
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