AI / GenAI Software Engineer (Junior / Associate)

Marktine Technology Solutions Pvt ltd; Jaipur; On-site; INR 600,000 - 1,200,000; Full time

Job summary

Marktine Technology Solutions Pvt ltd in Jaipur is seeking a Junior / Associate AI Software Engineer to build production LLM applications, RAG pipelines, and agentic workflows. The role emphasizes AI-assisted coding tools to accelerate the delivery of high-quality software.

You will work with Python, FastAPI, and various vector databases to deploy scalable AI features, ensuring robust testing and maintainability. A growth-focused environment awaits, with exposure to cutting-edge GenAI tooling.

Qualifications

• Solid foundation in Python (async, REST APIs, OO coding).
• Experience with GenAI/RAG concepts and embedding models.
• Familiarity with vector databases and LLM tooling.

Responsibilities

• Build end-to-end RAG pipelines (ingestion, chunking, embeddings, indexing, retrieval).
• Integrate LLM APIs (Claude, OpenAI, Gemini, open-source) into backend services using FastAPI or Python.
• Leverage AI-assisted coding tools to explore codebases, write tests, and speed up deployments.
• Design structured outputs, function calls, and agent workflows with LangChain/LlamaIndex or native SDKs.
• Implement basic evals, guardrails, and logging to control costs and reduce hallucinations.

Skills

Python

Git

GenAI & RAG

LLM tooling

Tools

Chroma

Qdrant

Pinecone

pgvector

LangChain

LlamaIndex

Job description

Marktine Technology Solutions Pvt ltd | Full time

AI / GenAI Software Engineer (Junior / Associate)

We are seeking an execution-focused AI Software Engineer to build and ship production LLM applications, RAG pipelines, and agentic workflows. We want a developer whose daily workflow is already supercharged by AI-assisted coding tools (Claude Code, Cursor, Copilot) to build, test, and ship clean software fast.

What You’ll Do

• Build, optimize, and maintain end-to-end RAG pipelines (document ingestion, chunking, embeddings, vector indexing, retrieval, and reranking).
  
• Integrate LLM APIs (Anthropic Claude, OpenAI, Gemini, open-source models) into scalable backend services using FastAPI or Python.
  
• Use AI-native coding setups (Claude Code CLI, Cursor, GitHub Copilot) to explore codebases, write unit tests, and accelerate deployment cycles.
  
• Design structured outputs, function/tool calling, and agent workflows using frameworks like LangChain, LlamaIndex, or native SDKs.
  
• Implement basic evals, guardrails, and logging to reduce hallucinations, measure retrieval quality, and monitor API costs.
  

Core Requirements

• Programming: Solid foundation in Python (async, REST APIs, clean object-oriented code) and Git.
  
• GenAI & RAG: Hands-on experience with vector databases (e.g., Chroma, Qdrant, Pinecone, or pgvector) and embedding models.
  
• AI Tooling Native: Daily, active user of CLI or IDE agentic coding tools (Claude Code, Cursor, or similar)—you know how to guide AI agents and rigorously inspect generated code.
  
• Proof of Work: At least 1 shipped or working project beyond basic chat (e.g., semantic search tool, custom RAG on documents, automated agent, or GitHub repo).
  

Good to Have

• Familiarity with Docker, Linux environment, and basic cloud deployment (AWS/GCP).
  
• Experience with Model Context Protocol (MCP) or multi-agent orchestration frameworks.
  
• Exposure to front-end integration (Streamlit, Next.js, or React basics).