Harika Yenuga
Currently leading GenAI at Macy's. Previously caught fraud at Bank of America and built retail ML at Apple. I ship AI that works Monday morning, not just in notebooks.
The Story Behind The Code
I spent 8 years learning that the hard part of AI isn't the model—it's everything else. Data quality. Latency. Edge cases. Stakeholder buy-in. Prod incidents at 3am. Built retail forecasting at Apple, fraud detection at Bank of America, and now GenAI infrastructure at Macy's. Each role taught me that shipping beats perfecting.
End-to-End Ownership
I own the full stack: data pipelines, model training, deployment, monitoring. No handoffs, no "that's not my job." When something breaks at 2am, I fix it.
Business First
I start with the problem, not the tech. Stakeholders don't care about your architecture—they care if it works. I translate ML capabilities into business outcomes.
Production or Nothing
Jupyter notebooks don't count. I measure success by what's running in prod, handling real traffic, making real money. MLOps isn't overhead—it's the job.
Where I Learned
The foundation of my journey
MS Business Analytics
Northwood University • GPA: 3.87/4.00 • 2024
B.Tech Electronics & Communications
JNTU • 2015-2019
Moments That Mattered
Milestones along the way
Research Publication
Predictive Hiring with ML • IBAC May 2025
Spot Awards (x2)
Apple Inc. • Exemplary Performance
Four Chapters,
One Journey
Every role taught me something different. Together, they shaped how I think about building AI that matters.
Macy's Inc.
CurrentLead AI Engineer
GenAI Platform & Applied ML Systems
This is where everything comes together. After years of building ML systems, I finally get to lead a GenAI transformation from scratch. The challenge is not just technical but cultural: how do you help 500+ stores trust and adopt AI? Every day brings new questions, and I love that.
Started with a blank canvas. Built the entire LLM platform from zero, designing RAG architecture that now answers questions across millions of product documents in under 2 seconds
Sub-2s LatencyCreated autonomous AI agents that actually think. They decompose problems, pick the right tools, write SQL, and correct their own mistakes. Watching analysts save 60% of their time never gets old
60% Time SavedBuilt trust through safety. Our guardrails ensure 99.9% of responses are safe, because in enterprise AI, one bad answer can undo months of adoption work
99.9% SafeMade deployment boring (in the best way). CI/CD, versioning, monitoring all working seamlessly so the team can focus on building, not firefighting
Production MLOpsBank of America
Senior AI Engineer
ML Engineering & Financial Risk Systems
Banking taught me what it means when your code really matters. When your model flags a transaction as fraud, you are either saving someone their life savings or blocking a legitimate purchase. That pressure shaped how I think about every system I build now.
The fraud problem was personal. Built models that cut false alerts by 35% while catching more actual fraud. Behind those numbers are real people who kept their money or did not get wrongly blocked
35% Fewer False PositivesLearned to think in petabytes. Built pipelines processing more data in a day than most companies see in a year. The scale was humbling and addictive
PB-Scale EngineeringGot obsessed with deployment speed. Reduced model releases from weeks to hours because in fraud detection, every day of delay means more money lost
Hours Not WeeksLed the GenAI charge before it was cool. Built document AI systems that 200+ analysts now rely on daily. Seeing skeptics become believers is still the best feeling
200+ Daily UsersApple Inc.
AI Engineer
Retail Analytics & ML Systems (via Infosys)
Apple changed how I think about quality. Their bar for everything is impossibly high, and it is contagious. I came in to build dashboards and left knowing how to lead teams, ship ML systems, and demand excellence from myself.
My dashboards reached executives. Built pipelines processing 50M+ daily transactions so leadership could see every store on the planet in real-time. No pressure, right?
50M+ Daily RecordsConvinced the team to go from reports to predictions. Those forecasting models cut stockouts by 25%. Somewhere, a customer got the iPhone they wanted because of math we wrote
25% Better ForecastsDiscovered I love teaching. Mentored 19 engineers and watched them grow. Turns out the best way to learn is to help others learn
19 Engineers MentoredTwo Spot Awards taught me something important: ship quality work ahead of schedule, and trust follows. Those awards opened doors to bigger challenges
2x Recognition AwardsRSI Softech
Data Engineer
National Remote Sensing Centre (Govt. of India)
Every journey starts somewhere. Mine started with satellite imagery from space and a lot of messy data. I did not know it then, but processing images of cities from orbit would teach me to love the puzzle of making sense of massive, complex data.
Built my first terabyte-scale system. Designing geodatabases for Smart Cities felt like science fiction. Watching urban planners use data I processed to improve real cities? Pure magic
TB-Scale DesignDiscovered automation is my superpower. Cut manual processing by 70% and suddenly had time to learn, experiment, and dream bigger
70% Faster ProcessingLearned that garbage in means garbage out. Taking data quality from 60% to 95% taught me to obsess over the fundamentals. That lesson still guides everything I build
95% Data QualityTechnologies I Think In
Every tool here has a story. Some I learned through late-night debugging. Others through production incidents. All of them shaped how I solve problems today.
Where I Live Now: GenAI
The Foundation: ML
Moving Data at Scale
Making It Run Anywhere
Languages I Dream In
Making AI Understandable
Where Data Lives
The Future: AI Agents
Curiosity Projects
That Became Real
These started as "what if" questions on quiet evenings. Some became research papers. Some became tools people use daily. All of them taught me something I could not learn at work.
MyFoodTracker
A Personal Problem That Became a Platform
It started with frustration. Existing nutrition apps felt slow and inaccurate. So one weekend, I decided to build something better. Six weeks later, I had a full AI platform: snap a photo, get instant nutrition data. The journey from "I can do this better" to "people are actually using this" was the most satisfying thing I have built.
What I Learned Building This
Thoughts & Publications
Sharing knowledge and insights on AI, ML, and data science
Revolutionizing Recruitment: Enhanced Machine Learning Models for Bias Mitigation and Efficiency
International Business Analytics Conference (IBAC) 2025
Submitted to Empirical Economics Letters (EEL)
Engineered end-to-end predictive hiring models achieving 97% accuracy while reducing bias in candidate selection.
View on GitHubLet's Write the Next Story
I'm looking for my next chapter: a team tackling hard problems with AI, where I can learn, contribute, and help ship things that matter. If that sounds like what you're building, I'd love to hear your story.
Every Great Thing Starts With a Conversation
Whether you are hiring, exploring a collaboration, or just want to talk about the future of AI over coffee, I answer every message. The best opportunities often come from unexpected conversations.