Excelra - Senior Artificial Intelligence / Machine Learning Engineer - Generative AI

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 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 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 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

(ref : hirist.tech)