AI/ML Implementation Engineer

About Innoterra :

Innoterra India Pvt Ltd operates high-velocity agri-tech and food businesses - fresh produce, dairy, animal nutrition, and distribution - managing procurement, quality, logistics, and last-mile supply chain at scale across India.

We are building an AI Decision Intelligence Platform that turns operational data into governed, real-time business decisions.

This is early-stage, high-ownership work. The platform and its direction are being shaped now, and the engineers joining at this stage will have a direct say in how it evolves - choosing tools, shaping architecture, and making decisions that will be in production for years.

Who We Are Looking For :

• Thinks analytically - breaks a problem down before writing code; asks why before asking how.

• Gets things done - ships working solutions without waiting for perfect requirements or perfect infrastructure.

• Owns the outcome - monitors what they build, fixes it when it breaks, and cares whether it works in production.

• Learns fast - picks up a new tool, library, or concept and applies it without hand-holding.

• Communicates clearly - explains technical decisions to non-engineers, and business tradeoffs to engineers.

• Has opinions - voices a reasoned point of view on tooling and approach - and is open to being wrong.

If you read documentation before asking questions, write a test before calling something done, and get genuinely curious when a pipeline produces the wrong number - this role is for you.

What You Will Do : AI and ML Implementation :

• Work across the AI/ML lifecycle - data preparation, feature engineering, model training, evaluation, and production deployment.

• Train, fine-tune, and experiment with models across problem types : classification, regression, time-series forecasting, and optimisation.

• Work with large language models - prompt engineering, fine-tuning, and building grounded RAG workflows.

• Take models from experimentation into production : versioning, serving, monitoring, and iterating on real-world feedback.

• Contribute to decisions on model selection, tooling, and AI approach - and stay current enough to have informed opinions.

Data Pipelines :

• Design and build pipelines that move, transform, and validate data reliably from source systems into AI-ready formats.

• Work with batch and event-driven patterns - choosing the right approach for each use case.

• Ingest from internal operational systems and external feeds : market prices, weather APIs, commodity data.