Generative AI Engineer

Generative AI Engineer

Key Responsibilities

• Design, build, and deploy generative AI models using frameworks like

  LangChain

  or

  LlamaIndex

  .
• Apply

  prompt engineering

  techniques and optimize LLM-based workflows for diverse business scenarios.
• Build

  Retrieval-Augmented Generation (RAG)

  pipelines using vector databases such as

  Pinecone

  ,

  Weaviate

  ,

  Chroma

  , or

  PGVector

  .
• Work with

  embeddings

  and similarity search to provide context-aware AI solutions.
• Develop and maintain scalable

  backend services

  using

  Python

  and frameworks like

  FastAPI

  .
• Design and expose

  RESTful APIs

  for seamless integration across services.
• Integrate with relational and NoSQL databases (

  PostgreSQL

  ,

  MongoDB

  ) and vector databases for intelligent retrieval.
• Ensure clean, maintainable, and asynchronous backend code using modern development practices.
• Leverage

  Docker

  for containerization and ensure scalable, secure deployments.
• Collaborate with cross-functional teams and translate AI research into production-ready applications.

Job Requirements

• Educational Background:

  Bachelors/Masters degree in Computer Science, Engineering, or related fields.

• Programming:

  Strong proficiency in Python; hands-on experience with

  FastAPI

  ,

  OpenAI API

  , and

  Hugging Face Transformers

  .

• AI/ML Expertise:

• Experience with

  LangChain

  ,

  prompt chaining

  , memory, tools, and agents.
• Strong understanding of LLMs, embeddings, RAG systems, and vector databases (

  FAISS

  ,

  Weaviate

  ,

  Supabase

  , etc.).
• Knowledge of

  Agentic AI

  and autonomous tool workflows.

• Backend & System Design:

• Experience in API design, microservices, and scalable system architecture.
• Database proficiency in

  PostgreSQL

  ,

  MongoDB

  , or

  DynamoDB

  .

• Cloud & DevOps:

• Experience with

  CI/CD pipelines

  ,

  Git

  ,

  Docker

  , and cloud platforms (

  AWS

  ,

  Azure

  , or

  GCP

  ).

• Soft Skills:

• Excellent communication and problem-solving skills.
• Proven ability to work in collaborative environments and/or lead engineering efforts.

Nice to Have

• Experience with

  LLMOps

  tools or platforms.
• Exposure to

  LLM fine-tuning

  ,

  data labeling

  , or

  custom model training

  .

  Role & responsibilities