🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
AI frameworks like LangChain, LlamaIndex, and n8n are quickly becoming the default way developers build with AI — and MongoDB wants to be the data platform that shows up everywhere they build. As part of the AI Builders Experience (ABX) org, we're standing up a brand new engineering team in Gurugram to make that happen, and we're looking for Senior Software Engineers to be among its founding members. The team owns the connective layer between MongoDB and third-party AI frameworks, platforms, and tools — the integrations that let developers use MongoDB effectively with the AI stack they've already chosen. That means shipping into some of the fastest-moving open-source ecosystems in tech, working mostly in Python and TypeScript, and partnering with the Database Experience (DBX) org when an integration needs new driver, platform, or product capability underneath it. This is a self-sufficient team by design. You and your engineering manager will be based in Gurugram, and the group is set up to own its area end to end: to decide how integrations get built, tested, released, and maintained, without waiting on another time zone to unblock the day-to-day. You'll work closest with the engineers sitting next to you, pairing on hard problems, reviewing each other's code, and dividing up a broad portfolio, while collaborating with product, partners, field teams, and open source maintainers as the work requires. You'll integrate with fast-moving, often unproven technologies, make pragmatic calls in the face of ambiguity, and own high-visibility projects with minimal guidance. Priorities can shift week to week based on framework changes, customer demand, and partner needs, so we're looking for product-minded engineers who thrive on autonomy and take pride in shipping. Much of your work will happen in public: sending pull requests to upstream repositories, working through external maintainer review cycles, and representing MongoDB in developer communities. MongoDB engineering teams pride themselves on building high-quality software and living our cultural values every day — we value intellectual curiosity, intellectual honesty, customer empathy, and building together in an environment that prioritizes collaboration over competition. We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Position expectations Design, build, test, and ship MongoDB integrations across a broad AI framework ecosystem that includes technologies like LangChain, LangGraph, LlamaIndex, n8n, and Mastra, owning them from first exploratory spike through production release and ongoing maintenance. Take ownership of ambiguous problems and drive them to completion independently, making sound decisions as requirements, frameworks, and customer needs evolve. Do the initial, exploratory work: assess a new framework, define the smallest useful version of an integration, get it in front of users quickly, and make a clear recommendation about whether and how to invest further. Contribute high-quality code upstream to repositories MongoDB doesn't own, maintaining a high standard of professionalism and courtesy while working through external maintainer review cycles. Build and maintain the team's engineering practices for a broad integration portfolio, including CI/CD, automated testing across framework and server versions, version compatibility awareness, and release readiness. Own the operational health of the integrations you build — monitoring, upgrades, breaking-change response, issue triage, and follow-through after launch. Partner with the Database Experience (DBX) org to define the driver, platform, and product capabilities your integrations depend on, making the case for what's needed and why. Work with product management, product design, partners, field teams, and open source maintainers to define the first version and future evolution of these integrations. Raise the technical bar within the team: shape architecture and development practices, give thorough code reviews, share what you know, and mentor peers and less experienced engineers. Stay current with the Python, TypeScript, and broader AI builder ecosystems and bring what you learn back to the team's roadmap. Bring a research and discovery mindset: notice emerging opportunities in the ecosystem, surface them early, and make the case for where MongoDB should show up next. Flag issues, risks, and resource needs early and clearly, both within the team and to cross-functional partners in other regions. Qualifications Strong background in building core components for scalable, high-availability services, developer platforms, or libraries 5+ years of experience building backend systems or developer-facing libraries, with strong proficiency in Python and/or TypeScript Proven success in designing, writing, testing, debugging, and performance tuning software in large, long-lived code bases, including code that other developers depend on Track record of identifying problems, implementing solutions, and delivering complex projects independently with minimal guidance A product-minded approach to engineering, with the judgment to make pragmatic tradeoffs between fast time-to-market and long-term maintainability, and the comfort to operate amid ambiguity and rapidly evolving technologies Demonstrated ability to context switch across a portfolio of projects while maintaining sound prioritization and quality. Strong and demonstrated interest in modern AI builder workflows and the developer tooling landscape: AI frameworks, agentic patterns, RAG and retrieval, embedding, vector search, or adjacent areas. Excellent verbal and written technical communication skills, including the ability to write things down clearly for partners you won't always overlap with in real time. Enjoys collaboration and being part of a close-knit team; is approachable, curious, and intellectually honest. Eager to learn, with a strong technical background. Nice to Have Meaningful open source contributions, especially work that landed in repositories you don't own and required navigating external maintainer review Professional experience building AI or framework integrations, MCP servers, agent skills, or plugins for agentic applications Familiarity with the agentic AI tooling ecosystem (AI IDEs, CLIs, and assistants) and how developers integrate with it Experience building and operating CI/CD and automated testing infrastructure for libraries with many downstream version and platform combinations Experience working with databases and knowledge of database internals; query optimization experience, especially with MongoDB Experience with vector search, embedding pipelines, or index management Working knowledge of Java, Go, C#, or Rust Experience with containerization and orchestration platforms (e.g. Docker, Kubernetes) and with AI frameworks and tooling Experience instrumenting usage telemetry and using it to guide investment decisions Familiarity with Jira and related tool Success measures In the first month, you will understand the team's scope, the high-level architecture of our integrations, where the relevant repositories and pipelines live, and the key technical dependencies around your work. You'll know who to go to in DBX and product, and you'll have landed your first meaningful code contribution In three months, you will own a significant integration or workstream end to end, have landed upstream contributions and established working relationships with the relevant external maintainers, and be contributing to how the team tests and releases its work In six months, you will have delivered a new integration or major capability that customers are using, improved the health and maintainability of the portfolio you own, and be a trusted voice in deciding where the team invests next In twelve months, you will be the recognized technical owner of a meaningful area of the portfolio, will have shaped architectural decisions and engineering practices beyond your own projects, and will be actively influencing the forward-looking technical strategy for how MongoDB shows up in the AI framework ecosystem 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- 3273512145
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.