Senior AI/ML Engineer

ABOUS US - Nomiso builds AI-powered products solving real enterprise problems — personalization, predictive operations, and intelligent automation. You will design and build ML pipelines, integrate real-time data streams via Kafka, and work with the Applied AI/ML Lead and Data Engineering team. You own features end-to-end from clustering and segmentation through to recommendations and predictive analytics — accountable for what they do in production, not just in testing.

WHO WE WANT - Someone who has genuinely built and shipped AI/ML models into production — not in a notebook, but in a live environment where customers depend on them. Who has felt the pain of a recommendation engine failing at scale, a pipeline silently breaking at 2am, or a model drifting post-deployment with no one noticing. Someone who closes the gap between data science and production and owns what the model does in the real world, not just in a test environment.

WHERE DO YOU COME FROM - IIT / NIT / BITS or equivalent, class of 2015 or later. 5+ years overall, 3+ in building and deploying AI/ML models for production. You have owned the full lifecycle: data pipelines, feature engineering, training, validation, deployment, and production monitoring. Proficient in TensorFlow, PyTorch, scikit-learn, and Python. You know what real ML infrastructure looks like — not just notebooks.

WHERE DO WE LOOK - companies where AI/ML runs in production and is business-critical — not a quarterly roadmap slide. Freshworks, Razorpay, Swiggy, Zomato, PhonePe, Meesho, Flipkart, Postman, BrowserStack, Sarvam AI, Krutrim, Sigmoid, ThoughtWorks — or serious applied ML teams inside MNCs where models ship and engineers are held accountable for production outcomes, not just experiment metrics.

WHAT KIND OF PERSON - obsessed with outcomes, not experiments. Knows that an 85% accurate model in production is worth infinitely more than a 96% accurate model still in a Jupyter notebook. Works closely with data engineers, product managers, and platform teams — understands the hardest part of ML is rarely the math. Believes AI is compressing quarters into weeks — Anthropic, Cursor, Sarvam AI are rewriting enterprise software at speed — and wants to be the engineer who makes that velocity real for customers, not one who watches from the sidelines.

WHAT DO YOU BRING - core: Python · TensorFlow · PyTorch · scikit-learn · Kafka for real-time pipelines. Useful: MinIO · AWS S3 · AWS SageMaker or equivalent cloud ML platforms. You will integrate AI models into production, collaborate with data engineering on data readiness and quality, implement AIOps to reduce MTTR, and design solutions across clustering, recommendations, and predictive analytics. segmentation, Qualification: Bachelor’s in computer science, Engineering, or related technical degree.