Senior Manager, Data Science and AI
HybridMumbai, Maharashtra, India
Job Summary
Own the end-to-end AI architecture for commercial initiatives, defining RAG systems, multi-agent orchestration, and scalable data pipelines from ingestion to production deployment. Lead the design of LLM integration strategies, including fine-tuning, prompt engineering, and hybrid cloud or self-hosted model configurations. Write, review, and maintain production-quality code across the AI stack, building MLOps pipelines for training, evaluation, and monitoring while developing APIs that connect AI capabilities to business tools. Set technical standards, conduct architecture reviews, and mentor senior engineers to elevate the Commercial AI team's capability. Translate complex technical concepts to stakeholders and represent the function in enterprise governance forums. Continuously evaluate emerging frameworks and drive proof-of-concepts to institutionalize architectural learnings and responsible AI practices.
Required Qualifications
- Bachelor's or master's degree in computer science/ engineering, Mathematics or a related technical field
- Equivalent demonstrated expertise in AI/ML systems architecture accepted in lieu of formal degree
- 9+ years of progressive, hands-on experience in software engineering, AI/ML, and data architecture
- consistent track record of building and shipping production systems
- Deep, practitioner-level expertise in Generative AI
- LLM integration
- prompt engineering
- fine-tuning
- context window management
- output guardrails
- Hybrid work location
Desired Qualifications
- Experience with LangChain
- Experience with LlamaIndex
- Experience with AutoGen
- Experience with CrewAI
- Experience with OpenAI
- Experience with Azure OpenAI
- Experience with AWS Bedrock
- Experience with GCP Vertex AI
- Experience with self-hosted models
- Experience with hybrid LLM deployment approaches
- Experience with data lakes
- Experience with feature stores
- Experience with vector databases
- Experience with structured data pipelines
- Experience with unstructured data pipelines
- Experience with MLOps pipelines
- Experience with model training
- Experience with model evaluation
- Experience with model versioning
- Experience with CI/CD for AI
- Experience with monitoring in production
- Experience with drift detection
- Experience with hallucination guardrails
- Experience with latency tracking
- Experience with CRM integration
- Experience with content platforms integration
- Experience with regulatory review systems integration
- Experience with schema design
- Experience with data quality
- Experience with data lineage
- Experience with data governance
- Experience with access controls
- Experience with embedding models
- Experience with semantic search
- Experience with knowledge bases
- Experience with RAG systems
- Experience with intelligent retrieval systems
- Experience with content generation
- Experience with summarization
- Experience with intelligent search
- Experience with data ingestion
- Experience with model selection
- Experience with RAG pipelines
- Experience with agent orchestration
- Experience with production deployment
- Experience with data architecture
- Experience with technical leadership
- Experience with cross-functional partnership
- Experience with innovation
- Experience with continuous improvement
- Experience with responsible AI practices
- Experience with bias evaluation
- Experience with output validation
- Experience with explainability
- Experience with ethical use frameworks
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