Machine Learning Engineer

A hybrid build + advise role, not a pure model-training seat. You'll productionize data science workloads on the cloud, build LLM/RAG solutions on enterprise data, and act as a customer-facing technical advisor and mentor.
Responsibilities:
Build and scale customer data science workloads; apply MLOps best practices to productionize them across domains.
Develop LLM solutions on customer data RAG on enterprise knowledge repos, natural-language querying of structured data, and content generation.
Advise data teams on architecture, tooling, and best practices.
Provide technical mentorship to the broader ML SME community.
Requirements:
3-5 years hands-on data science experience with pandas, MLflow, scikit-learn, gensim, NLTK, TensorFlow/PyTorch.
Production-grade ML deployment experience on AWS, Azure, or GCP including drift monitoring.
Modern NLP/LLM experience: vector databases, LLM fine-tuning, deployment via HuggingFace, LangChain, OpenAI.
Graduate degree in a quantitative field (CS, Engineering, Statistics, Operations Research) or equivalent practical experience.
Strong communication skills; able to teach technical concepts to both technical and non-technical audiences.
Ability to hit technical training/role outcomes within 3 months of hire.
Good to Have:
Apache Spark for large-scale distributed data.
Databricks platform experience.
2+ years customer-facing (pre-sales or post-sales).

Experience
3-5 yrs