Level

MongoDB

Software Engineer 3 - Enterprise Architecture

AI in this role

Design and prototype emerging AI capabilities, LLMs, and agentic systems as a software engineer in Enterprise Architecture.

langgraphpythontypescriptgorustfastapireactkubernetes
ragfine-tuningai-evaluation

MongoDB seeks an experienced Software Engineer to join Enterprise Architecture on a focused team dedicated to AI experimentation. You'll partner with teams across the organization, including Internal Engineering, to explore, prototype, and validate emerging AI capabilities before they become formal roadmap priorities.

We are looking to speak to candidates who are based in Gurugram for our hybrid working model.

Our ideal candidate:

  • Has 2-5 years of professional software engineering experience
  • Deep experience with at least one modern programming language (Python, Typescript, Go, Rust, etc.)
  • Strong technical judgment and the ability to independently solve complex engineering problems
  • Excellent communication skills and comfort collaborating across teams and disciplines
  • Comfortable operating in ambiguity, scoping and running experiments when the right approach isn't known yet
  • Hands-on experience prototyping with LLMs or agentic systems (evals, fine-tuning, RAG, tool use, orchestration frameworks)
  • Has the ability to iterate fast, specially in the case where requirements are not clear
  • Keeps up with emerging AI tooling and models, not just what's already mainstream
  • Collaborative, detail-oriented, and passionate about developing usable software

Bonus Round:

  • Experience building and maintaining full-stack applications, from front-end UIs to backend API to data pipelines
  • Experience designing systems or services used by other engineers or teams
  • Knowledge of any of the following technologies: Tanstack Start, Next.js, FastAPI, React
  • Experience with model evaluation frameworks, vector databases, or agent orchestration tooling (e.g., LangGraph)
  • Familiarity with transformer architecture and model internals
  • Familiarity with containerized applications, Kubernetes, and ability to design CI/CD pipelines

Position Expectations:

  • Design and run experiments that test emerging AI capabilities against real internal use cases, before they're formally prioritized. Condense the learnings of those experiments and document them so that the knowledge can be used across the org.
  • Build lightweight prototypes to validate or kill ideas quickly
  • Stay current on new AI tooling and models, and turn relevant developments into concrete internal proof points
  • Partner with product, platform, and internal engineering teams to hand off validated capabilities once they're ready to move from experiment to production
  • Maintain technical rigor and clear documentation of findings, even for work that doesn't ship
  • Identify and communicate risks, unknowns, and open questions clearly to stakeholders

Success Measures:

  • In three months, have run and documented at least one AI capability experiment, with a clear recommendation on whether and how to pursue it further
  • In three months, demonstrate the ability to move quickly from idea to working prototype
  • In six months, have influenced the org's roadmap or tooling direction by surfacing a capability two to three steps ahead of current need, backed by evidence from your experimentation

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 AI-native startups and approximately 75% of the Fortune 100, 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.

Requisition ID - 3273558097

How we rate this

Software Engineer 3 - Enterprise Architecture at MongoDB rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

RAGFine TuningAI EvaluationLangGraphPythonTypescriptGoRust

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. How do you decide that one model's output is better than another's for a given task?
  4. What's a project where you used LangGraph hands-on?
  5. Walk me through how you've used Python in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: RAG, Fine Tuning, AI Evaluation, LangGraph, and Python. 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.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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