DS/ML Intern

Ovii's Interpretation of the Role

Join Fi Money’s Bangalore engineering team as a DS/ML Intern, building and evaluating AI‑powered product features. You’ll design evaluation pipelines, optimize model routing, create trustworthy scoring systems, and experience the full MLOps lifecycle.

Role Snapshot

• Build AI feature evaluation pipelines
• Optimize model routing for cost, quality, and latency
• Develop statistical scoring and measurement systems
• Implement MLOps processes: versioning, monitoring, rollout
• Collaborate across product and engineering
• Onsite role in Bangalore

Must-Have Requirements

• Python
• SQL
• Applied statistics
• Hands‑on DS/ML experience
• hands‑on DS/ML experience
• applied statistics
• degree in Computer Science, Data Science, Machine Learning, or related field

Nice-to-Have Signals

• LLM familiarity
• Evaluation/observability tooling
• Information retrieval / entity matching
• LLM exposure
• evaluation tooling
• information retrieval

Work Setup

• Location: Bangalore, India
• Work mode: ONSITE
• Employment type: Intern

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Relocation
• Notice period
• Coding test
• Portfolio

What You'll Likely Work On

• Design and maintain evaluation backbones for AI features, including failure taxonomies and LLM‑as‑judge rubrics.
• Engineer model routing strategies balancing cost, quality, and latency, and run experiments to validate choices.
• Transform noisy real‑world signals into reliable scores using statistical rigor and calibration.
• Build and support MLOps pipelines: feature extraction, model versioning, rollout, and drift monitoring.
• Work closely with engineering to ensure low‑latency, reliable model serving in production.
• Contribute to moving heuristic approaches toward calibrated, monitored systems.

Good Fit If You Have

• Curious about product decisions beyond modeling
• Comfortable working across backend, frontend, and data pipelines
• Interest in AI evaluation and model performance monitoring
• Ability to apply statistical concepts to real data