Manager, AI Engineering - Analytics

WeHireYou; India; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Drata in India seeks a Manager, AI Engineering - Analytics to lead in-product analytics, write code, design systems, and ship production AI features with a tight team, while guiding the group toward leadership.

You will set the technical direction for AI-driven analytics, build natural language data experiences, and establish eval-driven development, collaborating with product and design to deliver measurable impact.

Qualifications

• Real AI engineering background with production experience.
• Experience shipping end-to-end AI features and agents.
• Strong knowledge of prompts, retrieval, and structured outputs.

Responsibilities

• Write code, design systems, and review PRs with the team.
• Lead a small team of engineers and grow it over time.
• Set technical direction for AI-driven analytics and data foundations.
• Build NL data experiences and ground AI responses in real data.
• Define evals and establish an eval-driven development workflow.
• Partner with Product to scope and deliver features with measurable impact.

Skills

Agent/LLM systems

Leadership

Prompting & retrieval

Production-grade systems

Data fundamentals

Tools

Python

PyTorch/TensorFlow

ML tooling

Job description

Manager, AI Engineering - Analytics

Our Mission & Values:

At Drata, we help companies earn and keep the trust of their users, customers, partners, and prospects. We’re the proof layer that shows great companies deserve the trust they aim to build.

We live our values every day. Built on Trust means consistency is everything. Act with Integrity by always doing the right thing. Being Customer-Obsessed keeps the people we serve at the center of our work. Competitive Fire drives us to push ourselves harder than anyone else. Diversity brings unique perspectives that lead to better solutions. Automation First ensures we save time and money by making efficiency a priority.

Our Culture & Work Style

Everything we do springs from:

• Be a Driver (Owner‑Operator Mentality): Own your work. Improve relentlessly. Deliver results.
• Move at Drata Speed (Precision & Velocity): Fast decisions. Quick learning. Immediate impact.
• Stay Mission-Driven (Customer‑Obsessed): Challenge assumptions. Deliver value. Stay hungry.

We pair that high-velocity culture with a thoughtful hybrid model because we believe flexibility and collaboration both matter. That’s why in the Bay we come together in-office Tuesday through Thursday our high‑impact collaboration days where teams align, strategize, and innovate. Mondays and Fridays are flexible, giving you space for focused work, balance, and autonomy.

If you thrive when you’re empowered, energized, and working with smart, mission-driven people, you’ll feel at home here.

Why Join The Drata Team?

• See the Speed: Watch our CEO, Adam Markowitz, discuss the hyper‑growth journey, from $0 to $100M ARR in just four years
• Hear the Voice of the Team: Explore our “Life at Drata” page for employee testimonials on our collaborative and the growth opportunities available.
• Experience the Impact: See why we are consistently recognized on Fortune’s Best Workplaces lists.
• Connect with Us on Socials: LinkedIn - follow us for company updates, employee stories, and career news.

at Drata. This team is responsible for the in‑product analytics and reporting experience our customers rely on to understand their compliance posture, surface insights from their Drata environment, and turn data into action.

This is a player‑coach role.

You will be writing code, designing systems, and shipping production AI features alongside a tight group of engineers, while also setting direction, unblocking the team, and growing into the leadership role. It is a great fit for a strong AI engineer who is ready to take their first formal step into management without giving up the keyboard.

The most important thing you bring is a real AI engineering background. You have shipped agents to production, you know what evals are and have built them, and you have strong data fundamentals to back it up.

What you’ll do:

Build Alongside the Team

• Stay deeply hands‑on by writing code, designing systems, and reviewing PRs
• Own critical paths and pair with engineers on the hardest parts of the product
• Keep close to the codebase and the customer experience even as the team grows
• Set the bar for engineering quality through your own work

Lead a Small Team

• Lead a small, focused team of engineers and grow it thoughtfully over time
• Set clear goals, run good 1:1s, and create an environment where engineers do their best work
• Give direct, useful feedback and help engineers grow in their careers
• Invest in the basics of management: hiring, performance, career growth, and team health
• Partner with leadership to grow into the formal management craft

Own the AI and Data Direction

• Set the technical direction for AI‑driven analytics and the data foundation underneath it
• Make pragmatic decisions across the stack, from data modeling to agent design
• Define multi‑tenant data access patterns that safely serve customer‑scoped data at scale
• Make sound build, buy, and adopt decisions for the team's tooling
• Stay current on developments in applied AI and bring relevant ideas back to the team

Build Natural Language Data Experiences

• Help shape and build features that let users ask questions of their data in natural language
• Ground AI responses in real data, handle ambiguity, and surface uncertainty appropriately
• Keep AI‑driven experiences fast, accurate, and trustworthy
• Iterate quickly with design partners to find what works in production

Make Evals a First‑Class Practice

• Build the evals, telemetry, and offline/online test loops the team relies on
• Establish eval‑driven development as the default workflow
• Define what “good” means for each AI feature and measure it rigorously
• Use eval results to guide model, prompt, and architecture decisions

Ship and Learn

• Drive end‑to‑end delivery from spec to GA
• Partner with Product on scope, sequencing, and tradeoffs
• Ship iteratively to design partners, instrument adoption, and learn from real usage
• Establish the metrics that prove the experience is delivering value

What you’ll bring:

AI Engineering

• Real AI engineering background with at least one agent or LLM‑powered system shipped to production end‑to‑end
• Working knowledge of prompts, tool use, retrieval, and structured outputs
• Understanding of latency, cost, and quality tradeoffs in LLM‑based systems
• Familiarity with the failure modes of AI features in the real world

Evals

• Hands‑on experience designing and building evals for AI systems
• Comfort with offline benchmarks, regression testing for non‑deterministic systems, and online feedback loopsAbility to articulate how to evaluate an agent before, during, and after launch
• Bias toward measurable...