AI/ML Engineer

Role: AI + Machine Learning Engineer

at Traya Health

Location: Mumbai / Bangalore (Hybrid)

Experience: 47 years (flexible for exceptional talent)

Why this role exists

Traya is an outcomes-led, personalized treatment company - not a cosmetic brand.

Hair regrowth takes months. Most people quit early.

Our biggest challenge is not whether the product works - it's whether people stay consistent long enough to see results.

What makes Traya rare is the data we sit on:

Deep diagnostic data (hair tests, root-cause profiles)

Longitudinal behavior data (daily routines, adherence logs, streaks)

Human interaction data (doctor notes, hair coach calls, chats, tickets)

Multichannel communication data (App, WhatsApp, push, calls)

Long-term outcomes data (scalp images, reorders, results over months)

We now want to build a unified intelligence layer that:

Predicts what each customer needs right now

Decides the next best action + channel

Powers AI experiences (chat, voice, automation) that feel human, timely, and helpful

This role is about turning data intelligence action outcomes.

What you'll work on (AI + ML)

This role is deliberately broad and high-ownership.

We already have a strong point of view on where AI and ML can help today but we're equally excited about what we haven't imagined yet. You'll have the space (and expectation) to discover new opportunities hidden in our data and turn them into real product and business impact.

Machine Learning & Decision Intelligence

Explore Traya's rich, longitudinal customer data to uncover patterns in behavior, adherence, engagement, and outcomes

Build models, heuristics, or learning systems that help the business:

1. Anticipate customer needs and risks
2. Decide when automation is sufficient and when human intervention adds value
3. Continuously improve decisions as more data and feedback flow in

Design systems that move us from static, rule-based workflows to learning-driven, adaptive decision-making

Work closely with product and CX teams to translate insights into shipped features and operational improvements

This could evolve into anything from prediction, ranking, optimization, experimentation, or entirely new decision frameworks depending on what you discover.

Applied AI (LLMs, Voice, Automation)

Experiment with and build AI-powered experiences across chat, voice, and internal tools

Use modern AI systems to:

1. Understand and summarize large volumes of unstructured data (text, conversations, audio)
2. Assist human teams (coaches, doctors, CX) by reducing cognitive and operational load
3. Create scalable, personalized customer interactions that feel timely and relevant

Prototype quickly, learn from real usage, and scale what works into production systems

Some of these may become customer-facing; others may quietly power internal workflows. The direction is intentionally open.

How to read this role

We don't expect you to do all of the above on Day 1.

We do expect you to:

Ask the right questions

Spot high-leverage opportunities in data

Choose the right level of sophistication for the problem

Build things that meaningfully change outcomes

This role will naturally evolve as you do.

What success looks like

Within 612 months, you will have helped Traya:

Improve early-stage adherence and reduce drop-offs

Increase long-term retention and reorder rates

Reduce unnecessary human effort while improving outcomes

Create AI experiences that customers trust, not ignore

Build a scalable intelligence engine that compounds with every new customer

If your work doesn't change customer behavior or business metrics, it doesn't count.

Who will thrive here

You'll love this role if you:

Enjoy working at the intersection of AI, ML, and human behavior

Care about shipping impact more than perfect models

Are excited by messy data and real-world constraints

Can think both systems-first and customer-first

Want ownership, not just tickets

Must-have skills

Strong ML foundations (classification, time-series, experimentation)

Hands-on experience with Python and ML frameworks

Experience taking ML or AI systems to production

Comfort working with large, noisy, behavioral datasets

Solid understanding of modern AI systems (LLMs, embeddings, prompt design)

Big plus if you have experience with

Recommendation systems / NBA frameworks

LLM orchestration, RAG, tool calling

Voice AI (ASR, TTS, call flows)

Healthcare, consumer subscriptions, or retention-heavy products

Experimentation, causal inference, or uplift modeling

Why Traya is a special place to build AI

Most AI roles:

Optimize clicks

Ship generic chatbots

Sit far from real outcomes

At Traya:

Your models decide when to talk, when to stay silent, and when to escalate to a human

Your AI systems directly impact health outcomes and trust

You're building a core intelligence layer, not a demo feature

If you want to build AI that actually changes lives - not just dashboards - this is it.