Data Scientist

About the Role:

AI Engineer

Key Responsibilities:

• Fine-tune and adapt

  open-source LLMs

  (e.g., LLaMA 4, Mistral, Falcon) using

  NVIDIA GPUs

  for domain-specific tasks.
• Design and build

  AI agents

  capable of reasoning, planning, and tool use using frameworks like

  LangGraph

  ,

  LangChain

  , and

  AutoGen

  .
• Implement structured, multi-step workflows for agents including memory management, tool calling, human-in-the-loop logic, and retry/error recovery strategies.
• Integrate

  proprietary LLMs

  (e.g., OpenAI, Claude) and Open Source LLM (e.g. Llama 4) for hybrid solutions using APIs and plugins.
• Develop scalable backend systems using

  FastAPI

  and containerized applications using

  Docker

  .
• Deploy and manage models on

  AWS

  ,

  Azure

  , or other cloud providers, with a focus on

  scalability and reliability

  .
• Monitor and optimize infrastructure to ensure performance under variable traffic loads.

Requirements:

• Strong experience in

  fine-tuning LLMs

  , optimized with

  NVIDIA GPU

  toolkits (CUDA).
• Proficient in building

  LLM-powered AI agents

  using tools like:

• LangGraph

  (for stateful agent workflows)

• LangChain

  (chains, memory, and tools)

• AutoGen

  or

  CrewAI

  (multi-agent collaboration)
• Solid grasp of agent orchestration concepts such as state machines, task decomposition, tool selection, and intermediate reasoning steps.
• Hands-on with

  OpenAI/Anthropic APIs

  , including function calling, tool use, prompt engineering and context management.
• Retrieval-Augmented Generation (RAG) pipelines with

  vector databases

  (e.g., Weaviate, FAISS, Pinecone)
• Experience building and exposing

  REST APIs

  using

  FastAPI

  , with production-grade containerization in

  Docker

  .
• Deep understanding of cloud deployment and

  infrastructure scaling

  using services like AWS EC2/S3, Lambda, Azure Functions, AKS, or ECS.
• Familiar with monitoring tools and CI/CD workflows (e.g., GitHub Actions, Prometheus, Grafana).

Nice to Have:

• Experience with:

• LangServe

  ,

  Semantic Kernel

  ,

  Haystack

  , or

  SuperAGI

• Open-source contribution in agent orchestration frameworks
• Exposure to event-driven architecture, streaming (Kafka/PubSub), and async task orchestration (Celery, Ray)

Preferred Qualifications:

• Bachelor's or master's in computer science, Machine Learning, or a related field.
• Previous experience deploying

  agent-based AI systems

  in production environments.
• Strong problem-solving, system design, and cross-functional collaboration skills are important.

What We Offer:

• Work on cutting-edge AI agent technology solving real-world problems.
• A collaborative and ownership-driven environment with growth opportunities.