Manager, Data Science and AI
HybridMumbai, Maharashtra, India
Job Summary
Build and deploy production-grade AI agents that automate commercial workflows, optimize channel investment decisions, and enable intelligent user interactions using multi-agent orchestration frameworks. Construct end-to-end RAG systems involving document ingestion, embedding pipelines, and vector store integration to power content generation and market intelligence. Develop data pipelines that ingest, transform, and serve structured and unstructured commercial data while ensuring compliance with privacy and regulatory requirements. Contribute to MLOps pipelines for model versioning, automated evaluation, and production monitoring, maintaining clean Python code and comprehensive documentation. Partner directly with commercial leadership and program managers to translate business goals into scalable technical solutions, participating in design reviews and collaborating with legal and privacy stakeholders.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field
- 6+ years of progressive, hands-on experience in software engineering, data science, AI/ML, or data engineering
- consistent evidence of building and shipping production systems
- Working, practitioner-level expertise in Generative AI: LLM integration, prompt engineering, context window management, and output validation/guardrails
- Hands-on experience building and deploying RAG architectures — including embedding model selection, chunking strategies, hybrid search, and retrieval quality evaluation
- Practical experience with agentic AI frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent): implementing agents, tool use, and memory in real applications
- Familiarity with vector databases (Pinecone, Weaviate, Azure AI Search, pgvector, Chroma, or equivalent) and semantic search
- Solid Python skills; proficiency in SQL; experience with at least one cloud AI platform (Azure OpenAI / Azure ML, AWS Bedrock / SageMaker, or GCP Vertex AI)
- Hands-on experience building data pipelines for AI workloads — ingestion, transformation, embedding, and serving of structured and unstructured data
- Exposure to MLOps practices: deployment pipelines, model monitoring, and evaluation in production environments
Desired Qualifications
- Master's degree preferred; equivalent demonstrated hands-on expertise in AI/ML systems accepted in lieu of formal degree
- Master's degree in Computer Science, Data Science, AI, or a related quantitative field
- Experience in commercial pharma, healthcare technology, or a regulated industry — with familiarity with promotional-review workflows, MLR processes, or GxP/HIPAA/data-privacy compliance
- Hands-on experience with commercial analytics use cases: marketing mix modelling, channel attribution, next-best-action systems, or AI-powered customer segmentation
- Experience adapting or fine-tuning open-source foundation models (Llama, Mistral, Falcon, or equivalent) for domain-specific applications
- Familiarity with responsible AI frameworks, bias evaluation, or AI governance tooling (e.g., Azure AI Content Safety, Guardrails AI, Giskard)
- Relevant certifications: AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, GCP Professional ML Engineer, or equivalent
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