AI/ML Expert

Role & responsibilities AI Strategy & Implementation

• Support the implementation of AI strategies and technical roadmaps across software development projects.
• Identify opportunities to integrate AI into development, testing, automation, and other SDLC workflows.
• Design and implement AI-powered solutions that improve engineering productivity, quality, automation, and efficiency.
• Collaborate with architects, developers, testers, and project teams to convert AI opportunities into practical technical solutions.
• Evaluate emerging AI technologies and recommend solutions based on technical feasibility, scalability, security, and business value.

AI Training & Engineering Enablement

• Train, mentor, and enable developers and testers on effective AI tools, techniques, and methodologies.
• Drive adoption of AI coding assistants, AI-powered testing tools, and AI-enabled software engineering practices.
• Develop technical demonstrations, guidance, reusable assets, and best practices to accelerate AI adoption.
• Help engineering teams identify and implement suitable AI tools for coding, QA, test automation, documentation, and other SDLC activities.

Client Consulting & AI Advisory

• Act as an AI subject matter expert in discussions with clients and internal stakeholders.
• Understand business and technology challenges and identify practical, high-value AI use cases.
• Translate complex AI concepts into clear business outcomes, value propositions, and potential ROI.
• Collaborate with clients to define actionable AI use cases, solution approaches, and implementation roadmaps.
• Support technical presentations, solution discussions, demonstrations, and AI capability showcases.

Proof of Concept & Innovation

• Design, develop, and evaluate Proofs of Concept (PoCs) using emerging AI and Generative AI technologies.
• Rapidly prototype AI use cases and demonstrate outcomes to engineering teams, leadership, and clients.
• Assess AI solutions for technical feasibility, scalability, security, data privacy, and business value.
• Document PoC outcomes, lessons learned, technical recommendations, and potential paths to production.
• Stay current with developments in AI/ML, Generative AI, LLMs, AI-assisted development, automation, and related technologies.

AI Advocacy & Continuous Improvement

• Showcase successful AI implementations, PoC outcomes, and best practices across engineering teams and leadership.
• Act as an AI champion, identifying opportunities for continuous improvement, innovation, and AI adoption.
• Contribute to internal AI knowledge repositories, technical documentation, reusable frameworks, and best practices.
• Help build an innovation-first culture around responsible and effective adoption of AI across the software engineering organization.

Preferred candidate profile We are looking for an AI-focused software engineering professional who combines hands-on technical expertise with strong consulting, communication, and problem-solving skills.
Education & Experience

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical discipline.
• 3-5 years of experience in software development, AI/ML, or related technology roles.
• Hands-on experience implementing AI/ML models or Generative AI solutions.
• Experience integrating AI capabilities into software applications, development workflows, or enterprise environments.
• Experience developing AI/ML prototypes, PoCs, or production-ready solutions.
• Exposure to enterprise or production AI implementations is preferred.

Technical Expertise

• Strong understanding of AI/ML and Generative AI concepts.
• Hands-on experience with AI tools and technologies supporting the Software Development Lifecycle (SDLC).
• Experience with AI coding assistants and AI-assisted software development.
• Knowledge of AI-powered testing frameworks, test automation, and AI-enabled QA practices.
• Strong programming skills in Python, Java, .NET, or equivalent modern programming languages.
• Understanding of MLOps principles and practices.
• Ability to assess emerging AI technologies and determine their suitability for engineering and business use cases.

Generative AI & Cloud PreferredExperience with one or more of the following will be highly valued:

• Generative AI and Large Language Models (LLMs).
• Prompt engineering and evaluation.
• Retrieval-Augmented Generation (RAG).
• AI agents and agentic workflows.
• AI-powered enterprise applications.
• AI platforms and services across Azure, AWS, GCP, or equivalent cloud environments.
• Integration of AI solutions with enterprise applications, APIs, databases, and development platforms.
• AI-assisted software development within DevSecOps and CI/CD environments.

Responsible AI & Enterprise Readiness

• Knowledge of AI governance, responsible AI, security, data privacy, and enterprise AI considerations.
• Ability to evaluate AI solutions not only for functionality but also for scalability, security, reliability, and production readiness.
• Experience working with technical PoCs, innovation initiatives, or emerging technology adoption.

Consulting & Leadership Skills

• Ability to translate complex AI and technical concepts into clear business value and ROI.
• Strong communication, presentation, and stakeholder-management skills.
• Ability to work effectively with developers, testers, architects, product teams, business stakeholders, and clients.
• Demonstrated ability to influence, mentor, and guide technical teams without direct authority.
• Strong ownership, initiative, analytical thinking, and problem-solving skills.
• Comfortable working independently while collaborating effectively in cross-functional teams.
• Strong curiosity and passion for continuous learning in a rapidly evolving AI landscape.

What We Offer

• An innovation-first environment focused on emerging AI technologies and methodologies.
• Opportunities to work on impactful AI/ML and Generative AI initiatives across enterprise projects.
• Direct exposure to clients and opportunities to contribute to AI adoption and transformation.
• A collaborative culture that supports continuous learning, experimentation, and professional growth.
• Exposure to global clients, diverse technology ecosystems, and emerging AI use cases.