Gen AI Engineer

Weekday AI; Pune District; On-site; INR 400,000 - 700,000; Full time

Job summary

Weekday AI in Bengaluru/Pune is seeking a senior Generative AI Engineer to design and deploy agentic applications powered by LLMs and RAG, while ensuring smooth integration with frontend interfaces.

You will build scalable AI workflows, implement advanced retrieval systems with vector databases, and collaborate with backend teams to deliver high‑quality product experiences.

Qualifications

• Strong expertise in LLMs, prompt engineering, and transformer architectures applied to real‑world apps.
• Hands‑on experience with RAG architectures, vector search, and embedding models.
• Proficiency in Python and libraries such as LangChain, Transformers, OpenAI SDK, or Hugging Face.
• Familiarity with agentic frameworks like AutoGen, OpenAI, CrewAI, or LangGraph to design autonomous workflows.
• Experience deploying AI models via APIs or microservices.

Responsibilities

• Architect and implement Generative AI solutions using LLMs (GPT, Claude, LLaMA) to deliver human‑like interactions.
• Develop and optimize RAG pipelines with vector stores (ChromaDB, Mosaic) for efficient retrieval.
• Build and maintain agentic workflows and AI agents with frameworks like LangGraph and OpenAI for scalable behavior.
• Perform vectorization and fine‑tuning with models (GTE‑large, BGE) to improve accuracy and context.
• Collaborate with backend and frontend teams to embed AI capabilities into products with reliable performance.

Skills

LLMs

Prompt engineering

Transformer architectures

RAG architectures

Vector search

Python

LangChain

Transformers

OpenAI SDK

Hugging Face

Agentic frameworks

Education

Bachelor’s or Master’s in Computer Science

AI/ML specialization

Tools

ChromaDB

Data-bricks Mosaic ML Vector Stores

LangGraph

AutoGen

CrewAI

OpenAI

Job description

This role is for one of Weekday’s clients

Min Experience: 3+ years
Location: Bengaluru, pune
JobType: full-time

In this role, you will transform cutting‑edge Generative AI technologies into seamless, intelligent user experiences. You will design and deploy agentic applications powered by LLMs and Retrieval‑Augmented Generation (RAG) pipelines, while ensuring smooth integration with responsive frontend interfaces.

Requirements

Key Responsibilities

• Architect and implement Generative AI solutions leveraging LLMs such as GPT, Claude, and LLaMA to deliver intelligent, human‑like interactions.
• Develop and optimize RAG pipelines using vector databases (ChromaDB for local development and Data-bricks Mosaic ML Vector Stores) to enable efficient knowledge retrieval.
• Build and maintain agentic workflows and conversational AI agents using frameworks like LangGraph and OpenAI, ensuring scalable and adaptive behavior.
• Perform vectorization and fine‑tuning with models such as GTE‑large and BGE to enhance accuracy and contextual relevance.
• Collaborate with backend and app teams to embed Generative AI capabilities into products, ensuring seamless integration and performance.

Required Skills & Experience

• Strong expertise in LLMs, prompt engineering, and transformer architectures, applying them to real‑world applications.
• Hands‑on experience with RAG architectures, vector search, and embedding models to build intelligent retrieval systems.
• Proficiency in Python and libraries such as LangChain, Transformers, OpenAI SDK, or Hugging Face for rapid prototyping and deployment.
• Familiarity with agentic frameworks like AutoGen, OpenAI, CrewAI, or LangGraph to design autonomous workflows.
• Experience in deploying AI models via APIs or microservices, ensuring scalability and reliability.

Preferred Skills

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or related fields.
• Practical experience with cloud platforms (AWS, GCP, Azure) to deploy and scale AI solutions.
• Knowledge of CI/CD pipelines to automate testing, deployment, and monitoring of AI services.

Must-have skills

Retrieval Augmented Generation, LLM, Gen Ai

Good-to-have skills

agentic workflows, Python