Python Developer (GenAI)
Senior full-stack GenAI engineer (10+ yrs) who designs and builds agentic AI applications that automate enterprise workflows: Python/FastAPI backends, LLM apps (GPT, Claude) with LangChain and RAG over vector databases, React/Streamlit UIs, all deployed on AWS with Docker, CI/CD and GitOps practices.
- Compensation
- Not disclosed
- Location
- Reston, United States
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 10+ years of software engineering experience
- 3+ years hands-on delivering GenAI-based enterprise applications
- Python backend development
- FastAPI
- Pydantic
- REST APIs, GraphQL, WebSockets
- LLMs (GPT, Claude)
- LangChain or Strands for AI agents and orchestration
- Retrieval Augmented Generation (RAG)
- NLP (Natural Language Processing)
- Vector databases (pgvector, Pinecone, Weaviate)
- Data analysis, preparation, and curation
- Document ingestion and knowledge base creation (SharePoint, Confluence, Jira)
- AWS (Bedrock, ECS Fargate, S3, Guardrails)
- PostgreSQL
- DynamoDB
- Ping Identity, OAuth2/OIDC, SSO, authentication & authorization
- CI/CD pipelines with Jenkins or GitLab
- GitOps practices
- Docker containerization
- Security vulnerability mitigation
- ReactJS or Streamlit
- Portkey (AI Gateway), Apigee (API Gateway)
Nice to have
- Experience building AI agents for enterprise automation
- Experience implementing AI guardrails and LLM governance frameworks
- Experience building enterprise copilots or knowledge assistants
- Familiarity with LLMOps and AI observability platforms
Full description
Senior Full Stack GenAI Engineer with 10+ years of experience to design and build agentic AI solutions that automate enterprise workloads and business processes.
The ideal candidate will have strong expertise in Python-based backend development, LLM-powered applications, cloud-native deployment, vector databases, and modern DevOps practices. This role involves building end-to-end AI systems that integrate with enterprise platforms, automate workflows, and deliver production-grade AI applications.
Key Responsibilities
- Design and develop agentic AI applications that automate enterprise workflows and decision-making processes.
- Build scalable backend services using Python, FastAPI, and Pydantic.
- Develop and deploy LLM-powered applications using models such as GPT and Claude.
- Build AI agents and orchestration workflows using LangChain or Strands.
- Implement Retrieval Augmented Generation (RAG) solutions using vector databases (pgvector, Pinecone, Weaviate).
- Perform data analysis, preparation, and curation to build high-quality datasets for AI and knowledge retrieval systems.
- Design and implement document ingestion pipelines for enterprise knowledge sources such as SharePoint, Confluence, and Jira.
- Deploy AI workloads on AWS (Bedrock, ECS Fargate, S3) with proper security and scalability practices.
- Develop and integrate enterprise APIs using REST, GraphQL, WebSockets, and web services.
- Implement secure authentication and authorization using Ping Identity, OAuth2, OIDC, and SSO.
- Build user interfaces for AI applications using ReactJS or Streamlit.
DevOps & Deployment
- Build and manage CI/CD pipelines using Jenkins or GitLab.
- Implement GitOps practices for automated deployments.
- Containerize applications using Docker and deploy to cloud platforms.
- Implement security best practices, vulnerability scanning, dependency management, and container security.
Required Skills
Backend & APIs
- Python
- FastAPI
- Pydantic
- REST APIs, GraphQL, WebSockets
GenAI & Agent Frameworks
- LLMs (GPT, Claude)
- LangChain or Strands
- Retrieval Augmented Generation (RAG)
- NLP (Natural Language Processing)