Senior Machine Learning Developer

About the job

About the Company We are seeking a highly skilled and experienced Senior Machine Learning Developer to join our dynamic team. The ideal candidate will have a strong background in machine learning (ML), deep learning (DL), and large language models (LLMs). They should be proficient in Python and possess expertise in conventional ML algorithms, DL techniques, LLM and prompt engineering. About the Role Key Responsibilities Responsibilities • 5–8+ years of experience in AI/ML engineering, with at least 4+ years in technical leadership or architectural role focused on LLM applications, RAG systems, and agentic frameworks. • Strong expertise in Large Language Models (LLMs), including experience with OpenAI, Anthropic, Google Gemini, Llama, Mistral, and fine-tuning techniques (LoRA, QLoRA, PEFT). • Proven experience building and deploying production-grade Agentic RAG systems with orchestration frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent. • Deep understanding of Model Context Protocol (MCP) architecture, server implementation, and integration patterns for enabling tool use and context sharing across LLM applications. • Strong expertise in vector databases (Pinecone, Weaviate, Chroma, Qdrant, FAISS) and embedding models for semantic search and retrieval optimization. • Hands-on experience with LLMOps and deployment pipelines using frameworks like MLflow, LangSmith, Weights & Biases, Vertex AI, SageMaker, Azure OpenAI Service, or equivalent. • Strong programming expertise in Python with proficiency in async/concurrent programming, API development (FastAPI, Flask), and experience with agent memory systems and conversation management. • Experience implementing security best practices for LLM applications, including prompt injection prevention, PII handling, content filtering, and secure RAG architecture. • Experience with cloud AI environments (AWS Bedrock, GCP Vertex AI, Azure OpenAI) and infrastructure-as-code for scalable agent deployment. • Proven ability to design and optimize retrieval strategies, including hybrid search, reranking, query expansion, and context window management for agentic workflows. • Excellent communication, stakeholder handling, and leadership skills with ability to translate complex agentic AI concepts into business value and architectural decisions.

Requirements

• Python
• Large Language Models
• Vector Databases
• LLMOps
• AI/ML Engineering

Preferred Technologies

• Python
• Large Language Models
• Vector Databases
• LLMOps
• AI/ML Engineering