Senior Data Scientist

Role & responsibilities

ob Description

Job Title: Senior Data Scientist

Location:  Bangalore

Business & Team: BB Advanced Analytics and Artificial Intelligence COE

Impact & contribution:

As a Senior Data Scientist, you will be instrumental in pioneering GenAI and multi-agentic systems at scale. You will architect, build, and operationalize advanced generative AI solutionsleveraging large language models (LLMs), collaborative agentic frameworks, and state-of-the-art toolchains. You will drive innovation, helping set the organizational strategy for advanced AI, multi-agent collaboration, and responsible next-gen model deployment.

Roles & Responsibilities:

• GenAI Solution Development:

Lead end-to-end development, fine-tuning, and evaluation of state-of-the-art LLMs and multi-modal generative models (e.g., transformers, GANs, VAEs, Diffusion Models) tailored for financial domains.

• Multi-Agentic System Engineering:

Architect, implement, and optimize multi-agent systems, enabling swarms of AI agents (utilizing frameworks like Langchain, Langgraph, and MCP) to dynamically collaborate, chain, reason, critique, and autonomously execute tasks.

• LLM-Backed Application Design:

Develop robust, scalable GenAI-powered APIs and agent workflows using FastAPI, Semantic Kernel, and orchestration tools. Integrate observability and evaluation using Langfuse for tracing, analytics, and prompt/response feedback loops.

• Guardrails & Responsible AI:

Employ frameworks like Guardrails AI to enforce robust safety, compliance, and reliability in LLM deployments. Establish programmatic checks for prompt injections, hallucinations, and output boundaries.

• Enterprise-Grade Deployment:

Productionize and manage at-scale GenAI and agent systems with cloud infrastructure (GCP/AWS/Azure), utilizing model optimization (quantization, pruning, knowledge distillation) for latency/throughput tradeoffs.

• Toolchain Innovation:

Leverage and contribute to open source projects in the GenAI ecosystem (e.g., LangChain, LangGraph, Semantic Kernel, Langfuse, Huggingface, FastAPI). Continuously experiment with emerging frameworks and research.

•Stakeholder Collaboration:

Partner with product, engineering, and business teams to define high-impact use cases for GenAI and agentic automation; communicate actionable technical strategies and drive proof-of-value experiments into production.

Essential Skills:

• 10+ years of hands-on experience in Machine Learning, Deep Learning, or Generative AI domains, including practical expertise with LLMs, multi-agent frameworks, and prompt engineering.

• Proficient in building and scaling multi-agent AI systems using LangChain, LangGraph, Semantic Kernel, MCP, or similar agentic orchestration tools.

• Advanced experience developing and deploying GenAI APIs using FastAPI; operational familiarity with Langfuse for LLM evaluation, tracing, and error analytics.

• Demonstrated ability to apply Guardrails to enforce model safety, explainability, and compliance in production environments.

• Experience with transformer architectures (BERT/GPT, etc.), fine-tuning LLMs, and model optimization (distillation/quantization/pruning).

• Strong software engineering background (Python), with experience in enterprise- grade codebases and cloud-native AI deployments.

• Experience integrating open and commercial LLM APIs and building retrieval- augmented generation (RAG) pipelines.

• Exposure to agent-based reinforcement learning, agent simulation, and swarm-based collaborative AI.

• Familiarity with robust experimentation using tools like LangSmith, GitHub Copilot, and experiment tracking systems.

• Proven track record of driving GenAI innovation and adoption in cross-functional teams.

Education Qualifications: Bachelors or Masters degree in Computer Science, Engineering, Information Technology, or a related technical discipline.

Desirable:

• Papers, patents, or open-source contributions to the GenAI/LLM/Agentic AI ecosystem.

• Experience with financial services or regulated industries for secure and responsible deployment of AI.