AI/ML Engineer

Role Overview

We’re building the small, senior AI team that builds these products. This is one of two core hands-on AI/ML engineer seats, working directly under our Principal AI Engineer. Expect your time to split roughly half model work, half backend/pipeline work.

Key Responsibilities

• Build and ship production models — object detection, segmentation, OCR/text extraction, and classification models behind our products.
• Build the AI backend the models live in. Run the models on incoming data, then write the post-processing and pipeline logic that turns raw model output into clean, structured product data. All in Python.
• Work in the data layer. Detected and human-corrected results are stored in a document store (MongoDB) — you design document structures and write the queries and aggregations your pipeline and the retraining loop depend on.
• Feed the data flywheel — the annotation → correction → retraining loop that makes the models better release over release.
• Own evaluation for your work — benchmarks, error analysis, and quality metrics tied to real product outcomes.
• Deploy and run your models and your pipeline code — Docker, Kubernetes on AWS EKS — and iterate on what production tells you.
• Work under the Principal AI Engineer’s technical direction, and partner with the Senior Applied ML Engineer on data quality and the eval harness.

Requirements

Must-Haves

• 3–5 years hands-on building production ML/AI — you’ve shipped models that real users or customers rely on.
• Strong Python for both model and product code.
• Strong PyTorch (or TensorFlow) and solid ML fundamentals.
• MongoDB: comfortable designing document schemas and writing non-trivial aggregation queries.
• PostgreSQL: working knowledge.
• Docker and Kubernetes (AWS EKS), and hands-on AWS experience.

Strong Plus

• Computer vision (detection/segmentation — YOLO, Detectron2, Mask R-CNN) or OCR / document AI.
• Geospatial / GIS exposure (imagery, GDAL/geopandas, remote sensing).
• MLOps depth — MLflow, model registry, monitoring, data/label versioning.
• RAG / GenAI / agentic exposure, or data-centric ML.
• Fluency with AI-assisted coding.

Location & Work Mode

This role is based in Bengaluru and follows a hybrid work model, with approximately 3 days per week in office and up to 40% work-from-home flexibility.