Role Description
The Senior Manager Generative AI (GenAI) Engineering will
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.
Technical Skills
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 programsable 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 35 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.
Experience and Background
- 12-16 years of professional experience in product engineering, digital solutions, or AI-led product development.
- Proven track record in leading teams, delivering AI/ML solutions, and managing engineering execution.
- Strong understanding of AI adoption frameworks, CoE practices, and engineering maturity models.
- Experience in client engagement, stakeholder communication, and technical consulting.
- Exposure to Agile methodologies, scrum leadership, and innovation-driven delivery.
Certifications (Preferred)
- AWS / Azure / GCP Certified Solutions Architect Associate or Professional
- Generative AI or Applied AI Certification (AWS, Google Cloud, Microsoft)
- Scrum Master / SAFe Practitioner certification desirable
- TOGAF Foundation preferred for architecture understanding.
