Manager | Data Science | Bengaluru | Finance Transformation
Deloitte (company site, opens in a new tab)9+ yrsBengaluruOn-siteDirect career pageEst. 30–50 LPA
Posted 1d ago
Reach people at Deloitte
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Deloitte you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Build and take AI/ML services from prototype to production, covering LLM workflows, retrieval, extraction, classification, ranking, analytics, and structured document or diagram generation.
- Develop robust APIs and hybrid RAG workflows with grounding checks and citations; partner with data engineering on indexing, embeddings, and retrieval performance.
- Own evaluation and iteration using automated checks and telemetry for quality, grounding, latency, and cost.
Minimum Requirements
- Work Experience Required: 9+ years; the JD specifies this alongside strong Python skills.
- Bachelor’s or master’s degree, or related fields.
- Strong Python (FastAPI, Pydantic, Pandas/Polars); LLM frameworks such as LangChain or Semantic Kernel, including function calling and structured outputs; cloud model hosting and embeddings/vector search experience.
- Practical ML for extraction/classification, evaluation and productionization; programmatic document generation and diagramming. Job location: Bengaluru; onsite requirement not explicitly mentioned in the JD.
Ideal Candidate Profile
- Has experience delivering production-grade AI services end to end, beyond prototypes.
- Combines LLM orchestration and hybrid RAG with applied ML, structured outputs, and measurable evaluation of quality, latency, and cost.
- Can work across application integration and data-engineering dependencies, including APIs, indexing, embeddings, and retrieval performance.