Senior Product Manager, Atlas Agent Engine
MongoDB is hiring a Senior Product Manager, Atlas Agent Engine in Gurugram, India. Level rates it ; you can apply on Level.
AI in this role
Lead product management for MongoDB's AI application platform, focusing on agentic workflows, retrieval, memory, and deployment patterns.
We're building a new product team in India to help shape how customers build, deploy, and operate AI applications on MongoDB. This is an early hire on a growing team—you'll join while the team is still taking shape, with the opportunity to have real impact from the start.
You'll report to a Director of Product Management based in Europe, working async-first with partners across India, EMEA, and North America. The engineering team in India is being built in parallel, so you'll be working closely with engineers from the early days of the team.
We are looking to speak to candidates based in Gurugram for our hybrid working model.
What you'll own
You'll be the end-to-end owner for a core area of our AI application platform—from discovery and vision through roadmap, delivery, and iteration. This is a senior individual contributor role where you lead through ownership, expertise, and cross-functional influence.
Day to day, that means
- Defining product strategy and roadmap for your area, aligned with MongoDB's broader AI strategy and business goals
- Talking to customers — from AI-native startups to large enterprises — to understand how they build and deploy AI applications, where they struggle, and what MongoDB should do about it
- Writing clear product narratives, memos, and specs that align stakeholders, clarify trade-offs, to guide conversations with engineering, design, product marketing, etc
- Partnering with cross-functional stakeholders—including Engineering, Design, Product Marketing, Partners, Sales, and Developer Relations—to scope, prioritize, and ship, balancing experimentation with reliability and scale, and driving positioning, launches, and enablement
- Identifying new opportunities autonomously—spotting gaps, shaping problem spaces, researching industry trends and running discovery sessions with customers
- Define and own clear success metrics for your area and use them to prioritize, make trade-offs and communicate impact
You'll develop deep expertise in how companies build AI applications — including agentic workflows, retrieval and memory, evaluation, observability, and deployment patterns.
How we work
- Async-first. Documentation and written communication are how decisions travel across time zones
- Sync when it makes sense. When we need to discuss something live, we hop on a call to unbundle issues together.
- In-person when it matters. The team gets together periodically for planning, relationship-building, and the kind of work that's better done face to face
What you'll bring
- 5+ years in product management (or equivalent) building platforms, infrastructure, or developer-facing products — ideally in cloud, data, or AI
- Proven end-to-end ownership of a product area: from framing the problem and vision through roadmap, execution, launch, and iteration
- Customer obsession: you're comfortable running discovery calls, digging into workflows, and turning messy feedback into clear product decisions
- Strong written and live communication: clear docs, productive meetings, confident stakeholder presentations. This matters more in an async, distributed team — writing is how you lead
- Technical depth sufficient to collaborate with senior engineers on distributed systems, cloud services, and AI/ML tooling, and to ask good questions when something is unclear
- Curiosity about AI: you follow how companies are building AI applications — agents, orchestration frameworks, RAG, fine-tuning, or other emerging patterns
- Comfort with ambiguity: you help define the problem, the success metrics, and the path forward. In an early-stage team, this is the job
Bonus points
- Experience building developer tools, AI platforms, or MLOps/LLMOps products
- Engineering background or deep experience partnering with engineering on technical products
- Experience with enterprise customers on mission-critical workloads
- Comfort using AI tools (e.g., Claude, Gemini, Figma Make) to move faster and amplify impact
How we'll measure success (30/60/90 days)
We need someone who can onboard quickly and start driving impact early.
In your first 30 days, you will:
- Ramp up on MongoDB products, AI application patterns, and how our teams work
- Shadow customer calls and internal forums to learn current strategy, open questions, and key stakeholders
- Produce a clear written overview of your product area: current state, key bets, and known gaps
By 60 days, you will
- Take ownership of a set of customer relationships and run your own discovery conversations each week
- Own and socialize an initial strategy and roadmap proposal for your area, with clear problem statements and success metrics
- Partner with engineering and design to shape and drive at least one scoped initiative into execution
By 90 days, you will
- Be the go-to owner for your product area, driving prioritization and day-to-day decision-making across functions
- Lead customer- and data-informed iterations on what you’ve shipped, adjusting the roadmap based on what you’re learning
- Contribute to the broader AI application platform strategy with perspectives from your domain, customers, and the field
Why this role
MongoDB is one of the most widely adopted database platforms in the world, used by tens of thousands of customers — including much of the Fortune 100 and a fast-growing set of AI-native startups. Our cloud platform, Atlas, runs across AWS, Google Cloud, and Azure.
This role puts you at the center of how MongoDB powers AI applications at scale. You’ll own a critical slice of that story, from strategy through delivery and adoption.
About MongoDB
MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the data platform for the AI era, enabling builders to create, transform, and disrupt industries with software. MongoDB’s unified data platform, the most widely available, globally distributed data platform on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud data platform and is available across AWS, Google Cloud, and Microsoft Azure.
With offices worldwide and over 67,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.
Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB.
To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!
MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.
MongoDB is an equal opportunities employer.
Req ID- 2273506592
How we rate this
Senior Product Manager, Atlas Agent Engine at MongoDB rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Tell me about a project where product management was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, Fine-tuning, ML Ops, and Product Management. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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