Machine Learning Architect

About the Role:

We are looking for a hands-on Machine Learning Architect / Senior ML Engineer to lead the design, architecture, and deployment of enterprise-grade AI/ML platforms and GenAI solutions. You will be responsible for building scalable data pipelines, productionizing RAG & Agentic AI workflows, and optimizing large-scale ML applications on Azure Databricks.

Key Responsibilities:

• Architecture & Solutioning: Design and architect production-grade GenAI, Multi-Agent systems, and enterprise RAG solutions on top of enterprise knowledge bases and Delta Lakehouse environments.
• Hands-on Development: Develop high-performance LLM workflows (LangChain, LangGraph, AutoGen) and build natural-language interfaces over structured enterprise databases/schemas.
• MLOps & Governance: Establish MLOps standards for model tracking, prompt versioning, registry, and drift monitoring using MLflow and Databricks Unity Catalog.
• Performance Optimization: Optimize serving-side latency and Time to First Token (TTFT) using advanced caching, streaming, and inference optimizations.
• Technical Leadership: Guide engineering teams on best practices across ML architecture, distributed compute (Spark), and scalable cloud infrastructure.Must-Have Core Requirements (Hard Filters):
• Databricks Project Work: Minimum 3+ years of native, hands-on project experience on Azure Databricks (including Databricks Vector Search, Delta Lake, and MLflow). Note: Profiles with only AWS/standalone script experience without Databricks project work will not be considered.
• Generative AI & Production RAG: Hands-on experience building, debugging, and productionizing Retrieval-Augmented Generation (RAG), hybrid retrieval (dense + sparse), and Agentic workflows (not just basic wrappers or chatbots).
• Distributed Processing: Deep proficiency in PySpark / Apache Spark for handling distributed, large-scale datasets.
• Core Machine Learning: 6+ years in traditional ML / Deep Learning tools (Python, PySpark, scikit-learn, PyTorch/TensorFlow).
• Education: Degree in Computer Science, Engineering, Statistics, or a related quantitative discipline.
• Notice Period: Candidates available Immediately or within 15–30 Days max will be preferred.Preferred / Good-to-Have Skills:
• Experience with Databricks Genie, Mosaic AI, or Unity Catalog governance.
• Experience in model latency optimization (reducing TTFT, streaming responses, KV-caching).
• Prior experience in client-facing advisory or technical leadership roles.What We Offer:
• Work on cutting-edge Enterprise AI, LLM, and Agentic AI projects at scale.
• Competitive salary band and growth opportunities.
• 100% direct/permanent employment with high-impact client engagements.How to Apply:

If you meet the Azure Databricks + Production GenAI/RAG requirements, click Apply Now or share your resume directly along with your Current CTC, Expected CTC, and Official Notice Period.