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 / Valuable-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 .

📌 Machine Learning Architect (India)
🏢 Recro
📍 India