Description

Key Responsibilities

- Develop and deliver GenAI/LLM solutions for problems such as content extraction, semantic search, question answering, summarization, reasoning, and recommendation.
- Design, deploy, and manage prompt-based and RAG-based systems, including orchestration patterns and agentic workflows (tool use, structured outputs, multi-step reasoning).
- Build comprehensive evaluation and testing frameworks (offline + online) to measure accuracy, faithfulness, robustness, latency, and cost; implement red-teaming and safety checks where applicable.
- Leverage Amazon Bedrock to prototype and productionize LLM applications, including model selection, prompt templates, routing, and deployment patterns.
- Work hands-on with Cortex (e.g., Cortex Analyst) to enable governed analytics experiences and GenAI-assisted workflows.
- Hands-on experience working in environments such as AWS Bedrock, Amazon SageMaker or Databricks
- Collaborate with engineering teams to deliver scalable services (APIs, batch jobs, pipelines), ensuring strong software engineering discipline and operational readiness.
- Build and maintain data pipelines for structured and unstructured data, enabling retrieval, indexing, and preprocessing for LLM applications.
- Conduct applied research by studying scientific articles and state-of-the-art techniques (prompting, fine-tuning, evaluation, agent design) and translating them into practical improvements.
- Communicate clearly with technical and non-technical stakeholders, translating business needs into measurable problem statements, solution designs, and success metrics.
- Mentor and lead junior data scientists, influence standards, and drive adoption of responsible AI practices.

Required Qualifications, Skills & Capabilities





- Advanced degree (Masters preferred) in Data Science, Computer Science, Machine Learning, Statistics, or related quantitative field (or equivalent practical experience).
- 5 -7 years of relevant applied experience building ML/NLP solutions, including production deployment in a fast-paced environment.
- Proven NLP + LLM experience, including prompt engineering, RAG, and evaluation methodologies.
- Hands-on experience with Amazon Bedrock (or equivalent managed LLM platform) for building and deploying GenAI solutions.
- Experience with Cortex (e.g., Cortex Analyst and related workflows) in an enterprise setting.
- Robust Python skills; familiarity with ML/DL frameworks such as PyTorch or TensorFlow, and standard ML tooling (pandas, NumPy, scikit-learn).
- Experience building APIs and integrating LLM/NLP solutions into applications and services.
- Data pipeline experience for structured/unstructured data processing; strong understanding of embeddings, vector search, indexing, and retrieval patterns.
- Solid software engineering practices: Git/version control, code quality, testing, and CI/CD fundamentals.
- Excellent communication and stakeholder management skills; ability to present tradeoffs, risks, and results concisely.
- Strong analytical skills and working knowledge of financial services / markets / asset management concepts.

Preferred Qualifications

- Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and related methods.
- MLOps experience: experiment tracking, model registry, monitoring, drift/performance tracking, incident management, rollback.
- Experience with cloud deployment patterns (AWS preferred) and production runtime environments (containers/orchestration).

📌 Vice President - Data Scientist Lead Role (Bengaluru)
🏢 JPMorganChase
📍 Bengaluru