Product Developer Architect

The Senior Manager Generative AI (GenAI)

Engineering willplay a pivotal role in driving the organization's AI-led product innovation and engineering execution.

Reporting to the Director / Senior Director – GenAI Engineering, this leader will be responsible for translating AI strategy into action, managing cross-functional engineering teams, and delivering GenAI-based solutions, frameworks, and proof-of-concepts (PoCs) across enterprise programs.

This role demands a hands-on engineering leader with strong expertise in AI/ML frameworks, full-stack product development, and cloud-native architectures (preferably AWS). The Senior Manager will guide a team of engineers, architects, and data practitioners, ensuring technical excellence, innovation, and delivery rigor.

The ideal candidate brings a product mindset, a passion for Experimentation AI-led transformation, and the ability to balance strategic thinking with deep technical execution.

1. AI Solution Delivery and Product Engineering

• Lead the design and implementation of 1-2 GenAI-driven solutions, PoCs, and client-facing prototypes aligned with the enterprise GenAI roadmap.
• Translate strategic AI initiatives into execution plans, managing delivery timelines, technical risks, and dependencies.
• Collaborate with the Director and teams to build AI accelerators, reusable components, and automation frameworks for internal and client use cases.
• Drive the integration of Generative AI capabilities into existing systems and applications — including intelligent assistants, document automation, and code intelligence.
• Drive engineering quality, code reviews, and technical standards for AI solution delivery.
• Contribute to architecture discussions, ensuring solutions are scalable, secure, and aligned with organizational standards.

2. Technical Leadership and Hands-on Contribution

• Build GenAI solutions, LLM-based applications, and prompt engineering frameworks hands-on using Python, LangChain, Hugging Face, and related ecosystems.
• Lead experimentation with fine-tuning, embedding strategies, and RAG architectures for enterprise use cases.
• Act as the technical anchor within GenAI programs—able to guide teams and contribute directly when required.
• Participate in architecture reviews and ensure adherence to AI governance principles.
• Collaborate with data engineering and DevOps teams to ensure smooth AI/ML model deployment pipelines (MLOps / AIOps).

3. Talent Development and Team Leadership

• Manage and mentor a team of AI engineers, full-stack developers, and architects.
• Foster a learning culture, encouraging engineers to explore new GenAI tools, frameworks, and problem-solving methods.
• Conduct regular code walkthroughs, design reviews, and innovation sessions to enhance team capability.

4. Collaboration and Stakeholder Management

• Engage with clients and internal stakeholders to understand business problems and propose AI-driven solutions.
• Collaborate with cloud, security, and infrastructure teams to ensure smooth deployment of AI applications.
• Partner with leadership to present technical outcomes, PoC results, and capability showcases.

Technical Prowess

1. Generative AI and ML Expertise

• Hands-on experience with AI development frameworks: LangChain, LlamaIndex, Hugging Face, OpenAI APIs, PyTorch, TensorFlow.
• Experience in prompt engineering, RAG pipeline design, vector database integration, and LLM tuning.
• Exposure to Agentic AI architectures, AI guardrails, and self-healing systems.

2. Product and Platform Engineering

• Experience in software product engineering, with at least 3–5 years in AI/ML solution delivery.
• Strong background in MACH Architecture, full-stack development, and cloud-native application design.
• Proficiency in Python, , Java, or similar technologies for backend; and React, Angular, or for frontend.
• Experience in DevOps/MLOps/AIOps pipelines, CI/CD, and containerized environments (Docker, Kubernetes).
• Proficiency in databases (SQL, NoSQL, graph, vector stores) and data modelling for AI use cases.

3. Cloud and Infrastructure Skills

• Strong command of AWS ecosystem (SageMaker, Bedrock, ECS, Lambda, Redshift, KMS).
• Experience deploying solutions on atleast one cloud ( AWS, Azure, or GCP) environments.