Machine Learning Engineer — Recommendations, LLMs & Self-Improving Systems

We're building the future of personalized, real-time digital experiences at a scale that few teams in the world get to operate at. If you're a Data Scientist who lives at the intersection of classical ML and next-gen AI, we want to talk.

What You'll Do

This isn't a maintain-the-status-quo role. You'll design, build, and deploy intelligent systems that directly shape how millions of people experience personalized content every single day.

Recommendation Systems & Classical ML

Design and scale large recommendation systems using advanced ML, ranking algorithms, and deep learning

Build models on high-volume, diverse data for personalization, prediction, and content understanding

Own the full ML lifecycle data prep, training, evaluation, and deployment

Run rapid experiments to validate hypotheses and drive measurable business impact

Monitor model health using statistical techniques; catch drifts and failure modes before they matter

Agentic AI & Next-Gen Systems

Build autonomous agentic systems that observe model performance, trigger experiments, tune hyperparameters, and orchestrate ML workflows with minimal human intervention

Apply LLMs, embeddings, RAG architectures, and multimodal generative models for semantic understanding and user preference modeling

Design intelligent agents that automate decision-making: candidate generation tuning, feature selection, context-aware content curation

Experiment with reinforcement learning, contextual bandits, and self-improving systems for next-generation personalization

Who You Are

You're not just a model builder you're a system thinker. You get excited about autonomous systems, obsess over model quality, and aren't afraid to push the boundaries of what AI can do in production.

You're comfortable working in ambiguity, own your work end-to-end, and thrive in fast-moving, high-impact environments. Write to rukmini@careerxperts.com