Agentic AI Engineer

7 years

0 Lacs

Posted:19 hours ago| Platform: Linkedin logo

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On-site

Job Type

Contractual

Job Description

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.

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