GenAI/Cloud - Architect

The Senior Manager - Generative AI (GenAI) Engineering will play 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, Node.js, Java, or similar technologies for backend; and React, Angular, or Vue.js 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, IAM, KMS).
• Experience deploying solutions on atleast one cloud ( AWS, Azure, or GCP) environments.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.