AI ML Developer

Junior AI/ML Python Developer
Experience: 2–3 years
About the Role
We are looking for a Junior AI/ML Python Developer to build and maintain AI-powered applications, LLM integrations, and backend services.
The role focuses on Generative AI, Agentic AI, intelligent search/RAG, and Python backend development. The candidate should be comfortable working with both LLM APIs such as OpenAI and Anthropic and open-source LLMs, and should be able to contribute to existing projects with minimal supervision.
Key Responsibilities
Develop AI/ML applications and backend services using Python and FastAPI.
Build RAG pipelines using embeddings, vector databases, semantic search, and retrieval techniques.
Integrate and work with LLMs such as OpenAI, Anthropic, and open-source models.
Build AI agents and agentic workflows using frameworks such as LangGraph, LangChain, or CrewAI.
Work with tool/function calling, memory, multi-step workflows, and AI integrations.
Work with LLM inference and deployment technologies such as Ollama, llama.cpp, vLLM, and AWS Bedrock.
Contribute to LLM fine-tuning using LoRA/QLoRA and understand basic model quantization concepts.
Debug, optimize, and maintain existing AI and backend systems.
Write clean, maintainable, and well-tested Python code.
Requirements
2–3 years of software development experience, preferably in Python and AI/ML.
Strong Python programming skills and hands-on experience with FastAPI/REST APIs.
Practical understanding of Generative AI, LLMs, and Transformer-based architectures.
Experience with embeddings, vector databases, semantic search, and RAG.
Experience working with LLM APIs such as OpenAI, Anthropic, or similar platforms.
Understanding of AI agents, tool calling, and agentic workflows.
Experience with at least one framework such as LangGraph, LangChain, CrewAI, or LlamaIndex.
Good understanding of databases such as PostgreSQL, MongoDB, or similar.
Familiarity with Git and good software development practices.
Strong debugging and problem-solving skills.
Nice to Have
Experience with LoRA/QLoRA, PEFT, or LLM fine-tuning.
Familiarity with AWQ, GPTQ, GGUF, or other quantization techniques.
Experience with Ollama, llama.cpp, vLLM, or AWS Bedrock.
Experience with Qdrant, Pinecone, Weaviate, Milvus, pgvector, Elasticsearch, or OpenSearch.
Familiarity with Docker/Linux and cloud platforms.
Ideal Candidate
A developer with strong Python/backend fundamentals who has hands-on experience with Generative AI and Agentic AI and can work across LLM APIs, open-source models, RAG/search systems, and AI agent workflows.
The candidate should be able to understand an existing codebase, build features independently, debug issues, and quickly learn new AI technologies as required.
Tech Stack: Python, FastAPI, OpenAI, Anthropic, Transformers, LangGraph, LangChain, CrewAI, LlamaIndex, RAG, Vector Databases, Ollama, llama.cpp, vLLM, AWS Bedrock, PostgreSQL, MongoDB, Docker, Linux, Git.

Work arrangement
Hybrid