Jr. Gen AI Engineer

GS Lab GAVS
Posted on
GS Lab GAVS logo

Experience
5 - 10 yrs
Job Location
Pune, India
Vacancy
1
Designation
Junior Machine Learning Engineer
Job Type
Not specified

Job Description

Office location : Gurgaon

Role: Data Scientist

Experience: 35 Years

Key Responsibilities

Design, develop, and deploy Machine Learning and Generative AI solutions.

Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.

Develop AI agents using Agentic AI frameworks such as LangGraph, LangChain, CrewAI, or similar technologies.

Integrate AI agents with enterprise APIs, tools, databases, and external services.

Develop prompts, tool-calling workflows, and structured output pipelines for LLM applications.

Fine-tune, evaluate, and optimize LLM-powered applications for accuracy, latency, and cost.

Implement data preprocessing, feature engineering, and ML model training workflows.

Work with structured and unstructured datasets to solve business problems.

Collaborate with Product Managers, Software Engineers, and Subject Matter Experts to deliver AI-driven features.

Monitor model and agent performance and participate in troubleshooting and continuous improvements.

Write clean, maintainable, and well-tested Python code following engineering best practices.

Stay updated with the latest advancements in Machine Learning, LLMs, and Agentic AI technologies.

Required Technical Skills

Core Skills

Strong proficiency in Python

Machine Learning fundamentals

Natural Language Processing (NLP)

Generative AI and Large Language Models (LLMs)

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Embeddings and semantic search

Model evaluation and validation techniques

Agentic AI Frameworks

Hands-on experience with LangChain and LangGraph

Experience building AI agents with tool calling and workflow orchestration

Familiarity with CrewAI, AutoGen, Semantic Kernel, or similar frameworks

Understanding of agent memory, planning, state management, and multi-step reasoning

ML AI Libraries

Scikit-learn

XGBoost or LightGBM

PyTorch or TensorFlow

Hugging Face Transformers

OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, or similar LLM APIs

Vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, Milvus, or OpenSearch

Data Cloud

SQL and relational databases

Experience with AWS, Azure, or GCP

Docker and containerized deployments

Basic CI/CD knowledge

MLflow or similar experiment tracking tools

REST APIs/FastAPI for AI model deployment

Good to Have

Experience building production-ready AI or LLM applications.

Exposure to multi-agent systems and workflow orchestration.

Knowledge of Model Context Protocol (MCP).

Experience with AI evaluation frameworks and guardrails.

Understanding of MLOps and model monitoring.

Experience with fine-tuning techniques such as LoRA, PEFT, or QLoRA.

Experience with document processing, OCR, or document intelligence.

Experience in legal, regulatory, financial, healthcare, or publishing domains.
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