Agentic AI Developer – LLM Systems & Automation (3 to 5 yrs)

Agentic AI Developer – LLM Systems & Automation
Experience:  3–5 Years
Location:  Remote (WFH)
Mode of Engagement:  Full-time
No of Positions:  4
Educational Qualification:  B.E./B.Tech/M.E./M.Tech in Computer Science, AI/ML, or related
Industry:  IT – AI/ML & Automation Services
Notice Period:  Immediate Joiner
What We Are Looking For
AI & LLM Development:  Strong hands-on experience in building and fine-tuning LLM-driven and agentic AI systems using frameworks like LangChain, LlamaIndex, or DSPy, with focus on reasoning, memory, and tool chaining.
Backend Engineering:  Solid Python development skills with expertise in designing FastAPI/Django-based APIs, managing data pipelines, and integrating LLM modules into scalable backend systems.
Autonomous Execution:  Proven ability to independently design, implement, and deploy GenAI workflows — taking ownership from prototype to production without senior handholding.
Responsibilities
Design, build, and optimize LLM-powered agentic systems that leverage memory, context reasoning, and dynamic tool invocation.
Implement multi-agent orchestration workflows integrating APIs, databases, and scrapers using frameworks like LangChain, LlamaIndex, or AutoGen.
Develop modular AI pipelines and integrate them with backend services using Python, FastAPI, or Flask.
Work on model evaluation, context handling, and prompt engineering to enhance reasoning and accuracy.
Deploy, test, and scale models on Dockerized or cloud environments (AWS/GCP).
Stay aligned with emerging trends in LLM reasoning frameworks, agentic design, and open-source AI ecosystems.
Qualifications
3–5 years of experience in AI/ML, backend, or applied LLM system development.
Strong Python programming skills with exposure to Transformers, LangChain, LlamaIndex, or DSPy.
Understanding of LLM architectures, tool chaining, context memory, and reasoning strategies (ReAct, Tree of Thought, etc.).
Experience with FastAPI, Docker, PostgreSQL, Git, and production-grade code practices.
Ability to independently manage AI projects from concept to deployment.
Strong analytical, debugging, and problem-solving skills.
Good communication and collaboration skills in distributed environments.