AI Engineer

Duration of Engagement

Key Responsibilities

• Design, build, and deploy

  AI/ML models

   for classification, prediction, recommendation, and optimization.
• Build and deploy

  REST APIs

   for ML/AI models using frameworks such as

  Flask

   or

  FastAPI

  .
• Apply

  asynchronous programming

   and

  high-performance API design

  .
• Work with

  Generative AI frameworks

   (LLMs, Diffusion models, Transformers, etc.) to develop next-gen AI solutions.
• Build and orchestrate

  Agentic AI systems

   capable of reasoning, planning, and executing tasks autonomously.
• Fine-tune and optimize

  Large Language Models (LLMs)

   for domain-specific use cases.
• Manage

  model versioning, monitoring, and scaling

   for APIs in production environments.
• Implement

  API authentication and security

   (JWT, OAuth, etc.).
• Integrate AI models with

  APIs, databases, and enterprise systems

   for end-to-end workflows.
• Evaluate model

  performance, scalability, and reliability

  , ensuring alignment with business goals.
• Research, prototype, and productionize

  cutting-edge AI techniques

  .
• Collaborate with

  product managers, data scientists, and engineers

   to deliver scalable AI-driven applications.

Required Skills & Experience

• 4-6 years

   of hands-on experience in

  AI/ML engineering

  .
• Strong coding skills in

  Python

   (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• Expertise in

  Generative AI

   (LLMs like GPT, LLaMA, Claude, diffusion models, etc.).
• Experience with

  Agentic AI frameworks

   (LangChain, LlamaIndex, AutoGPT, CrewAI, or similar).
• Solid understanding of

  NLP, embeddings, and vector databases

   (Pinecone, Weaviate, FAISS, Milvus).
• Strong knowledge of the

  ML lifecycle

   (data preprocessing, training, evaluation, deployment).
• Expertise in building

  REST APIs

   with FastAPI/Flask and integrating them into applications.
• Experience with

  containerized solutions

   using

  Docker

   &

  Kubernetes

  .
• Deployment experience across

  APIs, containers, and cloud platforms

   (AWS/GCP/Azure).
• Proficiency in

  prompt engineering, RAG (Retrieval Augmented Generation)

  , and

  model fine-tuning

  .
• Strong

  problem-solving, debugging, and optimization skills

  .