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Senior Machine Learning Engineer / ML Architect

Recro India
Remote
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AI Summary

Build and scale customer data science workloads applying MLOps best practices to productionize models across diverse domains. Develop LLM solutions including RAG architectures, natural-language querying of structured data, and content generation. Requires 4-6 years (Senior) or 6+ years (Architect) of hands-on industry experience with pandas, MLflow, scikit-learn, gensim, NLTK, TensorFlow/PyTorch, and Databricks.

Key Highlights
Enterprise-scale Azure data platforms powering analytics, reporting, and AI-driven transformation
Production-grade ML deployment with drift monitoring on AWS, Azure, or GCP
Databricks Certification required or 1+ years of current hands-on Databricks experience
100% remote work on cutting-edge LLM/RAG systems for real enterprise data
Key Responsibilities
Build and scale customer data science workloads, applying MLOps best practices to productionize models across diverse domains
Develop LLM solutions on customer data — RAG architectures over enterprise knowledge repositories, natural-language querying of structured data, and content generation
Advise data teams on architecture, tooling, and best practices across the data science lifecycle
Technical Skills Required
Python MLflow Databricks
Benefits & Perks
100% Remote — work from anywhere
Nice to Have
Apache Spark experience at scale
Deep Databricks platform expertise

Job Description


Senior Machine Learning Engineer / ML Architect | Remote

Redefining how enterprises harness Data & AI


We're building enterprise-scale Azure data platforms that power analytics, reporting, and AI-driven transformation for organizations rethinking how they work with data. If you want your ML work to ship into production — not sit in a notebook — this is that role.


Why This Role Matters

We help organizations simplify complex workflows and adopt cutting-edge technology at scale. Our data science and MLOps practice sits at the center of that mission — turning customer data into deployed, monitored, production-grade AI systems.


What You'll Do

  • Build and scale customer data science workloads, applying MLOps best practices to productionize models across diverse domains
  • Develop LLM solutions on customer data — RAG architectures over enterprise knowledge repositories, natural-language querying of structured data, and content generation
  • Advise data teams on architecture, tooling, and best practices across the data science lifecycle


What You Bring

  • 4–6 years (Senior ML Engineer) or 6+ years (ML Architect) of hands-on industry data science experience with pandas, MLflow, scikit-learn, gensim, NLTK, and TensorFlow/PyTorch
  • Proven experience deploying production-grade ML on AWS, Azure, or GCP, including drift monitoring
  • Databricks Certification or an ML/Cloud AI Certification required — or 1+ years of current, hands-on Databricks experience in lieu of certification
  • Graduate degree in a quantitative discipline (CS, Engineering, Statistics, Operations Research) or equivalent practical experience


Nice to Have

  • Apache Spark experience at scale
  • Deep Databricks platform expertise


Why Join Us

  • 100% Remote — work from anywhere
  • Work on cutting-edge LLM/RAG systems for real enterprise data, not toy demos
  • High-impact team shaping how large organizations adopt AI

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