Full-stack Data + ML Engineer

1 week ago

Goregaon, Maharashtra, India Sashr Consultants Full time ₹ 15,00,000 - ₹ 25,00,000 per year

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.

Role & responsibilities:

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 documentation.

Preferred candidate profile

  • 4 - 6 years delivering production data systems and ML features.
  • Strong SQL + Python; hands-on with dbt and an orchestrator (Airflow/Prefect/Dagster).
  • Experience with a cloud DW (Snowflake/BigQuery/Redshift) and lake formats (Parquet/Delta/Iceberg).
  • ML toolchain: PyTorch/TF, scikit-learn, experiment tracking (MLflow/W&B).
  • Model serving know-how; comfort with Docker, CI/CD, and basic Kubernetes concepts.
  • Clear communication, documentation habits, and pragmatic trade-offs.

Nice to have

  • Streaming (Kafka/Flink/Spark Structured Streaming) or CDC (Debezium).
  • Feature stores (Feast) and vector DBs (pgvector/FAISS/Weaviate) for LLM/RAG.
  • Fintech exposure: lending/underwriting, bureau/alt-data ingestion, model risk controls, data retention & compliance.

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