AI Engineer

Mandate Requirement : Candidates must haveBackend experience with Typescript. Typescript proficiency is non-negotiable. Candidates should also possess good communication skills.

Area Experience target

Professional software engineering : Total exp. 5-8 years, out of which

TypeScript / Node.js : 3-5 years

AI/ML engineering : 2-4 years

Production GenAI / LLM systems : 1 - 3 years

Technical ownership / architecture : 2+years

Key Responsibilities

AI Feature Design &Delivery – Lead the design, build and operation of AI-

powered features using large language models, translating product ideas into

reliable, scalable production systems that deliver measurable impact.

LLM &; Agentic System Architecture – Design integrations and

architectures for LLM and agentic applications, balancing capability,

performance, cost, security, reliability and maintainability.

Technical Leadership – Provide technical direction on AI initiatives,

contribute to architecture and design reviews, and help other engineers make

sound technical decisions.

Prompt Engineering & Model Optimisation – Develop, test and refine

prompts, model interactions and orchestration approaches to improve

accuracy, safety, latency and cost efficiency.

Hands-On Engineering – Build and maintain backend services, APIs and AI

integrations using TypeScript, Node.js and modern frameworks. Python may

also be used where appropriate for AI engineering, experimentation,

evaluation, data processing or integration with AI/ML tooling.

Evaluation & Monitoring – Define and implement evaluation approaches for

LLM and agentic systems, including logging, metrics and experimentation, to

continuously improve quality and reliability in production.

Scalable AI Patterns – Define and evolve reusable components,

architectural patterns and engineering standards for building AI-driven and

agentic capabilities across the platform.

Security, Privacy & Cost – Make informed engineering decisions around

model risk, data privacy, tool access, operational resilience and commercial

constraints.

Mentoring & Knowledge Sharing – Support engineers through code

reviews, design discussions, pairing and knowledge sharing, helping raise

engineering quality across the team.

Cross-Functional Collaboration – Work closely with product, data, platform,

architecture and security teams to shape solutions that are valuable, feasible

and production-ready.

Knowledge, skills and experience required

Our tech stack includes TypeScript (Node.js and React), AWS, Kubernetes,

DataDog, GraphQL, PostgreSQL, Mongo and Kafka. Python may also be used

where appropriate for AI engineering, experimentation, evaluation and data-oriented

workflows.