Assistant Vice President – Risk Modeling Solutions, Full-stack GenAI

Jobtailor; Bengaluru; On-site; INR 3,200,000 - 5,200,000; Full time

Job summary

Jobtailor is seeking a senior AI/ML backend architect to lead the design and implementation of agentic AI workflows in the risk domain. The role focuses on Python backends, FastAPI APIs, and robust MLOps practices with Docker/Kubernetes in cloud environments.

You will drive end-to-end AI solutions, integrate multiple agentic frameworks, and optimize retrieval and memory layers for scalable, reliable systems. This is a hands-on leadership role in a complex, regulated domain.

Qualifications

• 7+ years blending software development and data science/machine learning.
• Expert-level Python development and scalable backend/API design (FastAPI preferred).
• Experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel.
• Experience architecting RAG systems and vector databases (OpenSearch, Pinecone, Weaviate).
• Containerized AI systems on AWS/Azure/GCP and Kubernetes deployment.

Responsibilities

• Architect Agentic Systems: design and lead multi-agent AI workflows with advanced reasoning and autonomous execution.
• Solution Design & Development: translate risk-domain problems into technical requirements and develop end-to-end AI solutions.
• AI Workflow Development: implement end-to-end agentic workflows focusing on reasoning, tool use, and memory.
• LLM Orchestration: build retrieval pipelines, memory layers, and tool-use sequences with LangChain.
• Backend & API Engineering: develop Python-based microservices and REST APIs using FastAPI.
• RAG Implementation: construct retrieval augmented generation pipelines with document ingestion and vector search.
• Containerization & Deployment: package AI services with Docker and deploy on Kubernetes; support CI/CD.
• Observability & Evaluation: instrument workflows with Langfuse and maintain evaluation harnesses.

Skills

Python development

Backend services

APIs (FastAPI)

LangChain/LangGraph

RAG systems design

Docker

Kubernetes

Cloud platforms (AWS/Azure/GCP)

MLOps & CI/CD

Observability & evaluation

Tools

Docker

Kubernetes

OpenSearch

Pinecone

Weaviate

AWS Bedrock

Azure AI Foundry

GCP Vertex AI

Job description

Responsibilities

• Architect Agentic Systems: Design and lead the implementation of complex, multi-agent AI workflows capable of advanced reasoning, planning, and autonomous execution using frameworks like LangGraph, CrewAI, and Google ADK.
• Solution Design & Development: Translate complex business problems within the risk domain into well-defined technical requirements, and develop robust, end-to-end AI solutions to address them.
• 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.

Requirements

• 7+ years of professional experience in a role blending software development and data science/machine learning.
• Expert-level Python development skills and a proven track record of designing and building scalable backend services and APIs (FastAPI preferred).
• Deep, hands‑on experience designing and building solutions with multiple agentic frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel).
• Extensive experience architecting and optimizing RAG systems and integrating with vector databases (e.g., OpenSearch, Pinecone, Weaviate).
• Proven expertise in designing and deploying containerized (Docker/Kubernetes) AI systems on a major cloud platform (AWS, Azure, or GCP).
• Strong experience implementing MLOps principles, including CI/CD, observability, and evaluation frameworks for LLM‑based systems.
• In‑depth understanding of AI risk, safety, and enterprise governance requirements.
• Strong background in ML, deep learning, and NLP, including Transformer architectures.
• Preferred Qualifications: Experience leading the design of LLM evaluation harnesses for automated release validation.
• Deep experience with Langfuse or similar AI observability and tracing platforms.
• Hands‑on expertise with AWS Bedrock, Azure AI Foundry, or GCP Vertex AI.
• Knowledge of GraphRAG patterns and advanced multi‑hop retrieval strategies.
• AWS certifications (e.g., Solutions Architect, AI/ML Specialty) or equivalent.
• Background in financial services or another highly regulated industry.

Core Competencies

Demonstrates expertise in architecting and implementing complex AI workflows using frameworks like LangGraph and CrewAI, with a strong focus on Python development, containerization, and MLOps principles. Proven ability to design scalable backend services and integrate advanced retrieval systems within the risk domain.