AI Engineer · Data Scientist · Hyderabad, India
AI Engineer building intelligent systems that work in the real world — production LLM apps, predictive models, and data products that solve practical business problems.
I care about AI systems that survive contact with real users — not just notebooks.
I'm Hrishikesh Kini, a Senior Data Analyst / Manager turned AI Engineer with 3+ years of hands-on experience spanning MLOps, LLMOps, and Generative AI. I take AI products end-to-end — from problem framing and data pipelines to model deployment, monitoring, and measurable business outcomes.
At ASBL, I've driven ₹200+ crore in revenue impact by leading AI initiatives across real estate and fintech: building LLM-powered chatbots, agentic workflows, and large-scale BI platforms while mentoring analysts on the team.
I care about shipping AI that works in production — not just notebooks. My toolkit spans Python, SQL, cloud platforms (AWS, GCP, Azure), and the modern LLM stack, applied to domains from NLP to computer vision.
Six projects I'd walk an engineering team through — each has a full write-up.
Real-time adenoid detection from endoscopy imagery. Built as an end-to-end MLOps system: versioned data with DVC, experiment tracking with MLflow, and containerized deployment — a reproducible pipeline for clinical computer vision, not a notebook.
GenAI assistant for financial documents. Retrieval-Augmented Generation over earnings reports and news, grounded summarization and Q&A — the LLM pattern I now ship for customer-facing products, deployed with a live front end.
End-to-end sales analytics for a hardware manufacturer: SQL cleaning and joins over messy transaction data, then Tableau dashboards for revenue trends, regional performance, and KPI tracking. Data products built for real decisions.
Predicting at-risk bank customers with ensemble models, then making predictions defensible with SHAP-driven explainability — because retention teams won't act on a score they can't read.
Collaborative-filtering recommendation engine over user ratings, deployed with a live web interface — a full cycle from similarity modeling to a product people can use.
Sequence modeling of stock prices with recurrent networks — an engineering deep-dive into time-series forecasting and the honest limits of predicting markets.
3+ years building and shipping production AI and analytics.
What I reach for daily, from GenAI systems to BI dashboards.
I write about data science, AI, and markets on Medium — practical, not academic.
Apr 2, 2026 · 6 min read
Survival analysis answers one of the most valuable questions in data science: modeling time until an event occurs with censored data.
Read on Medium →Mar 4, 2024 · 5 min read
As AI drives core business operations, explainability and transparency are essential for stakeholder trust and regulatory defense.
Read on Medium →Aug 6, 2024 · 7 min read
Data-driven sector momentum strategies and quantitative risk frameworks for navigating market complexities.
Read on Medium →Jul 14, 2024 · 6 min read
Using the Hurst exponent as a statistical indicator to differentiate between mean-reverting and trending market regimes.
Read on Medium →Jan 30, 2024 · 5 min read
Implementing mathematical position sizing models to preserve capital and optimize risk-adjusted returns.
Read on Medium →Dec 18, 2023 · 6 min read
How systematic algorithmic workflows and automated execution provide a statistical edge in high-stakes environments.
Read on Medium →Explore all publications and research on Medium →
Open to AI Engineer, Data Scientist, and applied ML roles — and to AI collaborations. I reply to every email.