[Hiring] ML Data Engineer @Smart Working Solutions

Role Description

As an ML Data Engineer, you’ll sit at the intersection of analytics, machine learning, and data engineering—owning insight generation, model operations, and productised data solutions. You’ll work closely with the ML Lead to improve existing AI models in production and develop new ones, covering the full lifecycle: specifying, developing, testing, analysing, and refining.

This is a mid-level, mostly independent role suited to someone who combines strong analytical thinking with hands-on technical execution. You’ll thrive if you’re detail-balanced, process-aware, and capable of delivering results with minimal supervision in a fast-moving, research-driven environment.

Responsibilities

• Run hypothesis-led analysis over large datasets to uncover trends, drivers of outcomes, and craft client-ready narratives.
• Build and maintain industry benchmark datasets that power reports/dashboards; keep definitions and versioning tight.
• Deliver clear, actionable Power BI reports for clients and internal stakeholders; maintain selected stand-alone reports in 3rd-party tools where required.
• Own performance dashboards, operational processes, model version control/registry, and experiment tracking.
• Monitor drift and bias, plan/validate improvements, and manage safe deploy/rollback.
• Keep the feature store fresh from published calls; ensure training data lineage and reproducibility.
• Partner on productised evaluations (automated tests, acceptance thresholds) and bias mitigation aligned to policy.
• Specify, design, and implement dashboards & reports within the KAI solution; integrate with portal and API surfaces.
• Collaborate with platform/DB teams on robust data integration & storage patterns across PostgreSQL/NoSQL and data lake assets.
• Support Copilot auto-report creation where suitable, ensuring source-of-truth metrics and governance.
• Run client-specific studies to test hypotheses and meet project goals; present findings to non-technical audiences.

Qualifications

• 5+ years — Python (pandas, NumPy, scikit-learn).
• 4+ years — Databases & LLM familiarity: PostgreSQL and DynamoDB (or equivalent), familiarity with LLM concepts and evaluation methods.
• 4+ years — Power BI & Excel stack: Power BI (data models, DAX), Power Query (M), and advanced Excel (pivots, complex formulas); other BI suites acceptable only with strong Excel experience.
• 4+ years — Data warehousing & ETL.
• 3+ years — Cloud data services & ML Ops tooling: exposure to AWS/Azure and ML Ops tools (feature store, experiment tracker, model registry, monitoring); GCP experience may be considered as a substitute.
• Working style — Self-sufficient; ideally has led self-contained aspects of a project.

Nice to have

• Experience with other BI suites (paired with strong Excel).
• Familiarity with automated bias detection and compliance in ML systems.
• GCP exposure as an alternative to AWS/Azure for cloud data services.

Benefits

• Fixed Shifts: 12:00 PM - 9:30 PM IST (Summer) | 1:00 PM - 10:30 PM IST (Winter).
• No Weekend Work: Real work-life balance, not just words.
• Day 1 Benefits: Laptop and full medical insurance provided.
• Support That Matters: Mentorship, community, and forums where ideas are shared.
• True Belonging: A long-term career where your contributions are valued.