AI Data Engineer

We are seeking an experienced AI & Data Engineering Resource Capability Specialist to lead workforce

strategy, skill capability mapping, and resource management for our growing AI, Machine Learning, and Data Engineering practices.

In this role, you will sit at the intersection of technical delivery and strategic operations. You will be

responsible for ensuring our projects have the right technical talent assigned at the right time, while

actively designing learning paths to upskill teams on emerging technologies (LLMs, GenAI, MLOps,

Responsibilities:

1. Technical Delivery & Project Execution

Delivery Oversight: Act as the technical anchor across active AI and Data Engineering

engagements, ensuring architecture, data pipelines, and AI models meet quality standards

and performance benchmarks.

Technical Risk Mitigation: Identify technical bottlenecks, data governance risks, and

deployment blockers early in the project lifecycle, collaborating with engineering leads to

implement fast solutions.

Standards & Best Practices: Enforce modern engineering frameworks across project teams,

including CI/CD for ML (MLOps), code reviews, data testing, security protocols, and ethical AI

standards.

2. Project Planning & Technical Scoping

Technical Roadmapping: Translate client business requirements into concrete AI and Data

Engineering project plans, deliverables, technical milestones, and sprint backlogs.

Resource Architecture: Determine the exact technical skill mix, team topology, and tool stack

needed to successfully execute specific project scopes.

Feasibility & Solutioning: Work with project leads during inception to validate technical

feasibility, assess data maturity, and select appropriate platforms (e.g., cloud stack, vector

databases, LLM orchestration frameworks).

3. Team Leadership, Mentorship & Technical Guidance

Hands-on Guidance: Provide day-to-day technical direction to Data Engineers, ML Engineers,

and AI Developers, helping them solve complex architecture, ETL, and modeling challenges.

Code & Architecture Reviews: Lead technical reviews to ensure pipelines, API integrations,

and AI models are scalable, efficient, and maintainable.

Team Empowerment: Establish a culture of technical excellence, psychological safety, and

innovation, guiding the team through complex technical transitions (e.g., migrating to

4. Upskilling & Technical Capability Development

Technical Learning Frameworks: Design and lead structured technical upskilling programs to

transition traditional Data Engineers into Modern Data & GenAI/ML Engineers.

Hands-on Labs & POCs: Drive internal technical initiatives, hackathons, and Proof-of-Concepts

(POCs) to give engineers practical experience with emerging tools (e.g., LangChain,

LlamaIndex, Databricks, Snowflake, MLOps tooling).

Certifications & Skill Progression: Establish clear technical competency paths and mentor team

members through cloud platform, data engineering, and AI/ML industry certifications.

Required skills: DE, AI strategy analyst (Python, SQL, AI Tools & Infra)