The Opportunity:
We are looking for a highly skilled Agentic AI Engineer to join our team specializing in building autonomous, goal-oriented AI systems. You will play a crucial role in shifting our AI strategy from passive LLM chatbots to proactive, multi-agent orchestrations. In this role, you will utilize deep experience in agent orchestration frameworks, RAG, knowledge graphs, and fine-tuning Small Language Models (SLMs) for edge deployment.

What You'll Work On:

• Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen.

• Develop and deploy multi-agent systems using Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols to facilitate communication, tool usage, and collaborative task solving.

• Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs (KG) to enhance reasoning accuracy and context retention.

• Fine-tune SLMs for specific domains and optimize them for edge device performance, including ONNX, GGML, or Ollama.

• Develop evaluation frameworks to test agent reliability, safety, and performance, moving from prototype to production, including ReAct loops and human-in-the-loop.

Join us. The world can’t wait.

You Have:

• 2+ years of experience in software development

• 2+ years of experience as an ML engineer building production-grade ML solutions using tools such as Docker or Kubernetes, including, GenAI, LLMs, DL, RL, AI agents, agentic workflows, or complex automation frameworks

• 2+ years of experience with LangChain, LangGraph, AutoGen, PydanticAI, CrewAI, or LlamaIndex

• 2+ years of experience working in cloud environments, including AWS and Azure and evaluating architectural tradeoffs and designing robust service-based software applications for scalable use

• Experience with MCP for tool integration and A2A for agent-to-agent collaboration and with RAG architecture and KG, including Neo4j or NebulaGraph

• Experience fine-tuning LLMs or SLMs using Hugging Face, PEFT, or LoRA, evaluating LLM performance and behavior through evaluations, and building observation layers for stakeholders, including Grafana, Langfuse, LangSmith, or Phoenix

• Knowledge of modern software design patterns, including microservice design or edge computing

• Ability to adapt in a rapidly changing environment and navigate ambiguity

• TS/SCI clearance with a polygraph

• Bachelor’s degree 

Nice If You Have:

• Experience deploying agentic systems in a production environment

• Experience deploying agents on edge devices such as Android or local models

• Experience integrating coding agents such as Cursor or Windsurf into an efficient development pipeline with measured results

• Experience with programming, including ML frameworks such as TensorFlow, PyTorch, llama.cpp, and vLLM

• Experience engineering AI capabilities in on‑premise or multi‑classification environments

• Possession of excellent verbal and written communication skills for client engagements, client-facing project work, and business development

• Master’s degree in a CS or AI field preferred; Doctorate degree in CS or Statistics a plus

Clearance:

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance with polygraph is required.