Agentic AI Engineer

Job Title : Agentic AI Engineer

Location:

Role Overview:

As an AI Systems Engineer, you will be responsible for designing, developing, and deploying intelligent, agent-driven AI systems within the healthcare sector, with a focus on enterprise-grade applications. Utilizing frameworks such as LangChain and LangGraph, you'll build autonomous agents to enhance decision-making, optimize workflows, and orchestrate intelligent services—all while ensuring compliance in a regulated environment.

Key Responsibilities:

• Architect and build autonomous AI agents using LangChain, LangGraph, or other relevant frameworks.
• Develop multi-agent workflows tailored to enterprise use cases, including data retrieval, task automation, and reasoning.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Pinecone, FAISS) and prompt chaining.
• Integrate Large Language Models (LLMs) for generative tasks, applying fine-tuning, safety guardrails, and memory systems as needed.
• Create Python-based microservices and APIs to facilitate agent interactions, inference, and orchestration.
• Deploy AI solutions using containerization technologies (Docker, Podman) and cloud-native infrastructures.
• Implement telemetry, observability, and governance measures, including model evaluation, bias testing, and safety checks.
• Collaborate closely with Data Science, MLOps, and Product teams to ensure the successful delivery of scalable AI solutions.
• Adhere to enterprise AI governance standards, including data privacy, compliance, and security protocols.

Experience & Qualifications:

• 7+ years of experience

  (including 3+ years in AI/ML and 1+ year in agentic AI).
• Strong proficiency in

  Python

  development.
• In-depth experience with

  agentic AI frameworks

  such as LangChain, LangGraph, AutoGen, etc.
• Hands-on experience with

  vector databases

  (e.g., Pinecone, FAISS) and

  RAG architectures

  .
• Solid understanding of

  LLMs

  , embeddings, prompt engineering, and memory systems.
• Experience with

  containerization

  (Docker, Podman) and

  cloud deployment

  .
• Proficient in

  telemetry

  ,

  observability tools

  , and

  AI governance

  (model evaluation, bias testing, safety checks).
• Strong problem-solving, communication, and cross-functional collaboration skills.

Preferred Qualifications:

• Prior experience in

  healthcare

  or other

  regulated industries

  (e.g., HIPAA, PHI).
• Familiarity with

  model audits

  ,

  AI safety

  , and

  bias mitigation

  strategies.
• Experience in

  LLM fine-tuning

  and optimization techniques (e.g., quantization, distillation).
• Knowledge of

  MLOps frameworks

  such as MLFlow and Weights & Biases.