Back
Strong employer brand but niche GenAI requirements reduce broad applicant density.
Role needs specialized LLM/RAG and regulated-risk familiarity, moderately reducing cross-industry transferability.
Explicit 4+ years plus mandatory FastAPI, Docker/Kubernetes, and LLM/RAG tool experience increases screening rigidity.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and deploy end-to-end agentic Gen AI workflows integrating reasoning, tool use, and memory using frameworks like LangGraph, CrewAI, and LangChain.
Build and maintain Python-based scalable microservices and REST APIs (FastAPI) to expose AI capabilities with containerized deployment using Docker and Kubernetes.
Implement and optimize Retrieval-Augmented Generation (RAG) pipelines including document ingestion, embedding, and vector database integration (Azure AI Search, Pinecone).
4+ years professional experience blending software development and data science/machine learning.
Strong Python programming skills, experience building production APIs with FastAPI and microservices architecture.
Proficiency with Docker, Kubernetes, and containerized deployments.
Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, CrewAI) and RAG architecture with vector databases (OpenSearch, Pinecone, Chroma).
Bachelor's degree in Computer Science, Engineering, Business, or related field.
Experienced full-stack AI engineer with deep understanding of tokenomics, model-tiering, and performance optimization in AI architectures.
Demonstrated proficiency in NLP fundamentals and Transformer models within regulated or enterprise environments, preferably financial services.
Able to maintain engineering standards through code reviews, documentation, and collaborative development practices using Git-based workflows.