Job Starus: Active
One of our client is hiring for Machine Learning Engineer
Key Responsibilities:
• Design, build, and deploy AI agents and agentic workflows for document understanding, communication automation, and knowledge retrieval
• Develop, fine-tune, and optimize LLM prompts, RAG pipelines, and memory-based systems for contextual AI
• Integrate and manage vector databases (e.g., Pinecone, Weaviate, FAISS) for scalable semantic search and retrieval
• Develop and maintain Python-based pipelines, APIs, and microservices for AI automation tasks
• Work with structured and unstructured data, including PDFs, emails, and text data, to generate actionable insights using Gen AI
• Collaborate with product, data, and engineering teams to design scalable AI solutions
• Evaluate and integrate external LLM APIs (e.g., OpenAI, Anthropic, Mistral) and open-source LLMs (e.g., Llama, Mistral)
• Monitor and optimize AI model performance, latency, and cost
• Stay updated on advancements in agentic AI, LLM frameworks, vector search, and Gen AI tools; recommend adoption where appropriate
Required Skills & Qualifications:
• 3+ years of experience in Machine Learning / AI engineering, with a focus on Gen AI and LLMs
• Proficiency in Python, including frameworks like LangChain, LlamaIndex, FastAPI, Pydantic
• Strong experience in prompt engineering and LLM integration (OpenAI, Claude, Mistral)
• Hands-on experience with vector databases (Pinecone, Weaviate, FAISS, Chroma)
• Experience building retrieval-augmented generation (RAG) systems
• Familiarity with agentic AI frameworks (LangGraph, AutoGen, CrewAI, AutogenStudio)
• Strong understanding of document processing including OCR, PDF parsing, embeddings, and summarization
• Comfortable working with APIs, webhooks, and automation of communication workflows
• Solid understanding of data structures, algorithms, and performance optimization
Nice to Have:
• Experience with LLM fine-tuning or model distillation
• Knowledge of Docker, Kubernetes, and cloud services (AWS, GCP, Azure)
• Familiarity with open-source LLMs (Llama, Mistral, Zephyr) and deployment using vLLM, Ollama, Modal
• Exposure to PostgreSQL, Redis, and task queues like Celery or RQ
• Experience with AI observability tools (LangSmith, Helicone, Phoenix)
• Ability to manage ambiguity and drive projects independently
• Passion for staying updated with AI research and applying it to real-world use cases