Full Stack AI Engineer

Titan Capital; Gurugram District; Hybrid; INR 1,800,000 - 3,200,000; Full time

Job summary

Titan Capital in Gurgaon seeks an AI-native engineer to own data, IT and AI resources, building production-grade AI solutions for our investment and operations teams.

You will deploy and monitor systems, design scalable APIs (Node.js/Express.js, MongoDB) and interfaces (React/Next.js), integrate LLMs, and raise the team’s technical bar while shipping at the pace of a small team.

Qualifications

• Two or more years of API and data modeling work.
• Experience shipping AI-native systems with real users.
• Hands-on with Node.js/Express.js and MongoDB in production.

Responsibilities

• Build solutions in collaboration with the team, owning some problems end-to-end.
• Own the operational foundation: deployment, CI/CD, monitoring, backups, access controls.
• Integrate LLMs and AI APIs into production apps with robust evaluation.
• Design scalable APIs and data models (Node.js/Express.js, MongoDB) and React/Next.js interfaces.
• Own production incidents and drive post-mortems.
• Raise the technical capability of the team and review non-engineers' work.

Skills

LLM integration

API design

Node.js

MongoDB

React/Next.js

DevOps

Security mindset

Prompt engineering

Data modeling

Production systems

Tools

SQL

CI/CD

Kubernetes

Git

Job description

Location: Gurgaon Sec 67, in office/hybrid | Experience: 2+ years | Type: Full-time

About

Titan Capital is a leading early-stage venture capital firm investing in India's most ambitious startups at the seed stage. With a portfolio spanning 300+ companies in consumer, fintech, SaaS, healthcare and emerging technologies, Titan partners closely with exceptional founders to build category-defining companies.

Our investment team builds its own solutions and MVPs - sourcing engines, portfolio trackers and diligence agents that the firm now depends on daily. We are hiring one go-getter AI-native engineer to own data, IT and AI resources at Titan: to develop the solutions the team needs, decide which prototypes merit investment, move them into a secure production environment, and build the architecture that enhances Titan’s AI-native capabilities.

What will you do?

• Build solutions in collaboration with the team. In many cases, you will own a problem from the requirement itself as the sole builder. In others, you will take a prototype the team has already built into production, working alongside whoever built it, understanding the intent, and agreeing jointly on what to harden, what to rebuild and what to retire.
• Own the operational foundation end to end: deployment, CI/CD, monitoring, backups, secrets management, role-based access and audit logging.
• Build the evaluation and guardrail layer. An agent that is correct eighty percent of the time on deal data remains a liability until its accuracy can be measured and bounded.
• Design scalable APIs and data models (Node.js / Express.js, MongoDB) and clear, responsive interfaces (React / Next.js).
• Own production incidents personally, from alert through resolution to post-mortem.
• Integrate LLMs and AI APIs into production applications: retrieval, agents, tool use, and structured extraction from unstructured documents.
• Work AI-natively yourself. Coding agents, evals and automation are how we expect this role to ship at the pace of a small team rather than one person.
• Maintain our data posture under India's DPDP Act and under LP security review.
• Work from problem definition rather than a specification. Translate the requirements of the investment and operations teams into technical solutions, and challenge them where the trade-offs warrant it.
• Raise the technical capability of the team. Review what non-engineers ship, provide patterns and guardrails, and ensure the team can keep prototyping.
• Study how leading teams are deploying AI, and bring the architectures worth adopting back to Titan Capital.

What are we looking for?

• An AI-native builder. You reach for LLMs, coding agents and automation as a default working method, not as an experiment..
• Demonstrated experience shipping LLM-based systems into production, with real users depending on them. Fluency in prompt engineering, retrieval design and evaluation. You can distinguish a production-ready model output from one that is not.
• Backend, where this role sits heaviest. Three plus years of experience building APIs and data models in Node.js / Express.js with MongoDB, working with SQL, and handling messy third‑party data. Job queues, retries, rate limits and schema drift are familiar territory.
• Frontend, React / Next.js. Pixel-perfect design work is not required. Interfaces that are fast, clear and difficult to misuse are.
• Genuine DevOps competence, including hands‑on responsibility for systems running in production.
• A working security instinct. You understand the cost of a credential in a repository, the case for role-based access, and the response required when a vendor token is compromised. Application security or ethical hacking experience is an advantage.
• Comfort inheriting code you did not write. Some of your work begins as someone else's prototype, and you can read it, respect the intent behind it, and rebuild it constructively.
• An ownership mindset that does not depend on structure around it.
• Strong collaborative skills. You can sit with a team member, extract the real requirement from an imprecise brief, and explain a technical trade-off without jargon.

Nice to have: founding engineer experience, internal tooling or data infrastructure, entity resolution and deduplication, exposure to venture capital or private markets.

• Initial screening: A conversation to understand your background, experience, and fit for the role.
• Technical rounds: 1-2 technical discussions, which may include live coding, system design, and practical problem-solving exercises.
• Take-home assignment: A practical assignment designed to understand how you approach an open-ended problem and translate it into a working solution.
• Final conversations: 1-2 discussions with the leadership team to assess mutual fit, working style, and alignment with the role.