Senior AIML Engineer Gen AI

Job Description

Job Summary: We are seeking a highly skilled and innovative Senior AI/ML Engineer with strong experience in building Generative AI solutions from MVP to production. The ideal candidate should have hands-on expertise in multi-agent orchestration, prompt engineering, RAG architecture, and advanced Python frameworks such as LangChain, LangGraph, and AutoGen. Experience with Databricks and tool integrations (e.g., GitHub, UI components) is essential. Key Responsibilities: Design and implement Generative AI solutions, taking projects from MVP to scalable production systems. Build, orchestrate, and optimize multi-agent architectures using frameworks like LangChain, LangGraph, and AutoGen. Integrate external tools and APIs into AI agents (e.g., GitHub, internal/external UI components). Lead the development and deployment of RAG (Retrieval-Augmented Generation) pipelines. Collaborate with cross-functional teams to align AI solutions with business goals. Leverage Databricks for scalable data processing, ML model development, and deployment. Develop, refine, and evaluate prompts for LLM-based tasks (Prompt Engineering). Monitor and optimize the performance and accuracy of AI systems in production. Required Skills & Qualifications: 5+ years of experience in AI/ML, with at least 2 years focused on Generative AI solutions. Strong programming skills in Python and proficiency in GenAI frameworks (LangChain, LangGraph, AutoGen). Experience with building and orchestrating multi-agent systems. Proven track record in taking GenAI-based MVPs to full-scale production. Hands-on experience in integrating AI agents with tools and platforms (e.g., GitHub, custom UIs). Solid understanding and implementation experience with RAG architecture and prompt engineering. Proficiency with Databricks for data and ML workflows. Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment. Nice to Have: Experience with vector databases (e.g., FAISS, Pinecone, Weaviate). Exposure to cloud environments (Azure, AWS, or GCP). Familiarity with CI/CD for ML pipelines.