Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand and Bengaluru metro increase competition, but niche GenAI requirements limit applicant pool.
Specialized GenAI, RAG, and regulated risk domain experience required, reducing cross-industry fit.
Explicit 6+ years, expert Python, specific GenAI frameworks, cloud, Kubernetes, and MLOps mandate strict filters.
Lead design and implementation of complex multi-agent AI workflows for risk management using frameworks like LangGraph, CrewAI, and Google ADK.
Develop and deploy end-to-end AI solutions including backend Python microservices/APIs (FastAPI) and Retrieval-Augmented Generation (RAG) pipelines integrating vector databases.
Manage containerized AI systems deployment on Kubernetes and implement MLOps practices including CI/CD, observability, and model evaluation frameworks.
6+ years of professional experience combining software development and data science/machine learning.
Expert-level Python development skills; proven experience building scalable backend services/APIs (FastAPI preferred).
Hands-on experience with agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen) and RAG systems integrated with vector databases (OpenSearch, Pinecone, Weaviate).
Experience deploying containerized AI systems (Docker/Kubernetes) on cloud platforms (AWS, Azure, or GCP).
Experienced in architecting AI solutions specifically for risk management or highly regulated industries.
Strong background in ML, deep learning, and NLP with proficiency in Transformer architectures and enterprise AI governance.
Proven ability to lead LLM evaluation and observability (e.g., using Langfuse), with knowledge of advanced retrieval strategies and cloud AI platforms (AWS Bedrock, Azure AI Foundry, GCP Vertex AI).