Jr. Gen AI Engineer

GAVS Technologies N.A., Inc; Gurgaon; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

GAVS Technologies N.A., Inc. is seeking a Jr. Gen AI Engineer in India (Gurgaon/Pune) to design, develop, and deploy ML and Generative AI solutions. You will build RAG pipelines, develop AI agents, and integrate them with enterprise APIs and tools, while crafting prompts and workflows for robust LLM applications.

The role emphasizes fine-tuning models, evaluating performance, and delivering production-ready Python code with strong engineering practices.

Qualifications

• 3–5 years of experience in ML/AI and data science.
• Strong Python, NLP, LLMs and RAG knowledge.
• Experience with LangChain/LangGraph and agent development.
• Familiarity with API/tool integrations and workflow orchestration.

Responsibilities

• Design, develop, and deploy ML and Generative AI solutions.
• Build RAG pipelines using vector databases and enterprise sources.
• Develop AI agents with LangChain/LangGraph/CrewAI or similar frameworks.
• Integrate agents with enterprise APIs, tools, and databases.
• Create prompts, tool-calling workflows, and structured output for LLM apps.
• Fine-tune and optimize LLM-powered applications for accuracy, latency, and cost.
• Write clean, tested Python code following engineering best practices.

Skills

Python

Machine Learning fundamentals

Natural Language Processing

Generative AI & LLMs

Prompt Engineering

RAG pipelines

Embeddings & semantic search

Model evaluation & validation

Agentic AI frameworks

Tool calling & workflow orchestration

APIs integration

Tools

LangChain

LangGraph

CrewAI

AutoGen

Semantic Kernel

OpenAI API

Pinecone

FAISS

Weaviate

Job description

# Job Description**Back** ## Jr. Gen AI Engineer04-08-2026 21:32:07Job\_3046013 - 5 years* Pune, Maharashtra, India (PUN)Office location : GurgaonRole: Data ScientistExperience: 3–5 YearsKey ResponsibilitiesDesign, 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 SkillsCore SkillsStrong proficiency in PythonMachine Learning fundamentalsNatural Language Processing (NLP)Generative AI and Large Language Models (LLMs)Prompt EngineeringRetrieval-Augmented Generation (RAG)Embeddings and semantic searchModel evaluation and validation techniquesAgentic AI FrameworksHands-on experience with LangChain and LangGraphExperience building AI agents with tool calling and workflow orchestrationFamiliarity with CrewAI, AutoGen, Semantic Kernel, or similar frameworksUnderstanding of agent memory, planning, state management, and multi-step reasoningML & AI LibrariesScikit-learnXGBoost or LightGBMPyTorch or TensorFlowHugging Face TransformersOpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, or similar LLM APIsVector databases such as Pinecone, FAISS, ChromaDB, Weaviate, Milvus, or OpenSearchData & CloudSQL and relational databasesExperience with AWS, Azure, or GCPDocker and containerized deploymentsBasic CI/CD knowledgeMLflow or similar experiment tracking toolsREST APIs/FastAPI for AI model deploymentGood to HaveExperience 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.Apply Now