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Job Overview : Full-Stack Gen AI Engineer - Model/Anlys/Valid Intmd Anlyst (C11)

The responsibility includes but not limited to the following activities :

- AI Workflow Development : Implement end-to-end agentic AI workflows using frameworks like LangGraph, CrewAI, and AutoGen, focusing on reasoning, tool use, and memory.
- LLM Orchestration : Build and optimize retrieval pipelines, memory layers, and tool-use sequences using frameworks like LangChain.
- Backend & API Engineering : Develop robust, scalable Python-based microservices and REST APIs using FastAPI to expose AI capabilities.
- RAG Implementation : Construct and refine Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding, and vector search integration with databases like Azure AI Search or Pinecone.
- Containerization & Deployment : Package AI services using Docker and deploy them on Kubernetes, contributing to CI/CD pipelines for smooth and reliable releases.
- Observability & Evaluation : Instrument AI workflows using platforms like Langfuse for tracing and debugging. Implement and maintain evaluation harnesses to ensure model quality and performance.
- Engineering Excellence : Actively participate in code reviews, contribute to technical documentation, and collaborate with team members to uphold high engineering standards.

Qualifications :





- 4+ years of professional experience in a role blending software development and data science/machine learning.
- Strong Python development expertise for AI systems and backend services.
- Experience building production APIs with FastAPI and microservices architecture.
- Proficiency with Docker and Kubernetes for containerized deployments.
- Hands-on experience with agentic AI or LLM orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, LlamaIndex).
- Practical experience with RAG architecture, including vector databases (e.g., OpenSearch, Pinecone, Chroma).
- Proven ability to design and implement cost-effective AI architectures, with a deep understanding of tokenomics, model-tiering strategies, and caching for performance and budget management.
- Familiarity with Git-based workflows and cooperative development practices.
- A solid understanding of NLP fundamentals and Transformer architectures.

Preferred Qualifications

- Experience with Langfuse or similar AI observability platforms.
- Hands-on use of a major cloud AI platform (AWS Bedrock, Azure AI Foundry, or GCP Vertex AI).
- Familiarity with enterprise data platforms like Snowflake or Redshift.
- Background in financial services or another regulated enterprise environment.

Education : Bachelor's degree in Computer Science, Engineering, Business, or a related field. Job Family Group :Risk Management

- Job Family : Model Development and Analytics
- Time Type : Full time
- Most Relevant Skills :Analytical Thinking, Credible Challenge, Data Analysis, Governance, Policy, Procedure, and Regulation, Risk Management Lifecycle.
- Other Relevant Skills :For complementary skills, please see above and/or contact the recruiter.

📌 Risk Modeling Solutions - Full-Stack Genai - Intermediate Analyst (Mumbai)
🏢 Citi
📍 Mumbai