Generative AI Engineer — LangChain & Agentic AI

Robotic Technologies Automation; Arlington (VA); On-site; USD 92,000 - 125,000; Full time

Job summary

Robotic Technologies Automation in Arlington, VA seeks an experienced AI/ML Consultant specializing in LangChain and Generative AI to design and deploy scalable enterprise AI solutions. You will implement RAG pipelines, multi-agent workflows, tool-calling, and robust APIs using FastAPI/Flask.

The role emphasizes hands-on development with Docker/Kubernetes, model integration via Azure/AWS, and applying MLOps practices for dependable AI deployments.

Qualifications

• Experienced AI/ML consultant with hands-on Python, LangChain, Generative AI, and Agentic AI expertise.
• Strong background in deploying enterprise AI solutions and production-grade systems.
• Familiar with MLOps, prompt engineering, and API-based integrations.

Responsibilities

• Develop and deploy scalable, production-ready Generative AI applications using Python.
• Build LLM-based solutions with LangChain, LlamaIndex, and Hugging Face.
• Implement RAG pipelines, embeddings, and vector database integrations.
• Develop Agentic AI solutions, multi-agent workflows, and tool-calling capabilities.
• Apply ML, NLP, and prompt engineering techniques.
• Build backend services and REST APIs using FastAPI and Flask.
• Integrate AI models using Azure OpenAI, Azure AI Foundry, and AWS Bedrock.
• Apply LLMOps/MLOps for deployment and monitoring, containerized with Docker and Kubernetes.
• Collaborate with Model Context Protocol (MCP) for tool and system integration.

Skills

Python

LangChain

Generative AI

Agentic AI

NLP

Prompt Engineering

REST APIs

Tools

FastAPI

Flask

Docker

Kubernetes

Azure OpenAI

Azure AI Foundry

AWS Bedrock

LlamaIndex

Hugging Face

Job description

Robotic Technologies Automation in Arlington, VA seeks an experienced AI/ML Consultant specializing in LangChain and Generative AI to design and deploy scalable enterprise AI solutions. You will implement RAG pipelines, multi-agent workflows, tool-calling, and robust APIs using FastAPI/Flask.

The role emphasizes hands-on development with Docker/Kubernetes, model integration via Azure/AWS, and applying MLOps practices for dependable AI deployments.