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Full Stack Data & ML Engineer

Full Stack Data & ML Engineer

SaShr ConsultantsMumbai
17 hours ago
Job description

We are looking for a Full-stack Data + ML Engineer (Mid-Level) to join our Data & ML Team. The ideal candidate will bring strong experience in building data pipelines, managing data infrastructure, and deploying ML models into production.

This is a hands-on, high-impact role that blends data engineering and machine learning to drive actionable intelligence across the business.

Data Engineering (50%) :

  • Design and build robust ELT / ETL pipelines from app, events, and 3rd-party data sources (batch & streaming).
  • Create well-modeled data layers (staging / marts) with testing, documentation, and version control (e.g., dbt).
  • Operate and optimize data warehouses / lakes, ensuring data lineage, quality checks, and secure access (PII compliance).
  • Contribute to observability, cost tracking, and on-call support for data pipelines.

ML / AI (50%) :

  • Frame business problems, prepare datasets, and train / evaluate ML models for production use.
  • Build and maintain inference services / APIs (e.g., FastAPI, Triton, KServe) with defined latency and cost targets.
  • Implement LLM pipelines (RAG), manage retrieval evaluation, prompt optimization, and safety guardrails.
  • Work on classic ML use cases such as risk scoring, recommendation, churn, uplift modeling, and A / B testing.
  • Monitor model drift, data integrity, and performance; maintain detailed runbooks and & Experience :
  • 4-6 years of experience delivering production-grade data systems and ML features.
  • Strong expertise in SQL and Python.
  • Hands-on experience with dbt and an orchestration tool Proficiency with cloud data warehouses (Snowflake / BigQuery / Redshift) and lake formats ML toolchain proficiency : PyTorch / TensorFlow, scikit-learn, MLflow / W&B.
  • Familiarity with model serving, Docker, CI / CD, and Kubernetes concepts.
  • Strong communication skills, documentation habits, and ability to make pragmatic trade-offs.

    • Nice to Have :

  • Experience with streaming frameworks (Kafka / Flink / Spark Structured Streaming) or CDC tools (Debezium).
  • Familiarity with feature stores (Feast) and vector databases (pgvector / FAISS / Weaviate) for LLM / RAG use cases.
  • Exposure to FinTech / lending domains, underwriting, bureau & alt-data ingestion, model risk controls, and data compliance.

    • What to Expect :

  • Onsite collaboration at our Mumbai office with product, risk, and engineering teams.
  • High ownership across the data ? intelligence ? product loop.
  • Opportunities to mentor junior engineers and grow into a lead role as the team scales.

    • (ref : hirist.tech)

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