We are looking for an AI Engineer to build and scale production-grade AI capabilities across our core backend services. You will design agentic workflows, integrate LLM-powered endpoints, and work on delivery lifecycle from prototype to production reliability.

Primary Scope

Design, implement, and maintain AI/LLM features across backend services.

Build secure, observable API integrations for AI use cases in consumer-facing journeys.

Partner with product, design, and frontend teams to ship AI features with measurable business impact.

Key responsibilities of the AI Engineer shall include (but not be limited to):

- Collaborate with Product and Design teams to identify and build agentic AI use cases for consumer-facing journeys.
- Design and implement agentic workflows with planning and execution loops, secure tool/function calling, and strong failure handling.
- Build RAG pipelines, including document ingestion, chunking, embeddings, vector indexing, retrieval tuning, and grounded responses with citations where required.
- Develop secure and scalable AI APIs, SDKs, and integrations for frontend, internal systems, data sources, and third-party tools.
- Implement authentication, least-privilege access, RBAC, rate limits, retries, timeouts, budget controls, and provider failover strategies.
- Apply safety and compliance guardrails such as PII redaction or masking, content moderation, audit logging, and policy-aligned access controls.
- Define and automate evaluation frameworks to measure task success, response quality, hallucination, latency, and cost.
- Set up observability for AI systems using traces, metrics, logs, dashboards, and cost or latency monitoring.
- Optimize reliability, performance, and cost through caching, queuing, retries, timeouts, and model/provider fallback mechanisms.
- Contribute to CI/CD, infrastructure-as-code,



automated testing, and production deployment processes.
- Drive production readiness through clear documentation, runbooks, staged rollout plans, and incident response support.
- Work closely with backend, frontend, and QA teams through sprint planning, technical design reviews, and code reviews.
- Write clean, well-tested, maintainable, and production-ready code.

Resource Skills Requirement

The AI Engineer shall possess the following qualifications:

- Proven experience designing, building, and operating agentic AI/LLM systems in production
- Strong proficiency in Python; familiarity with TypeScript/Node.js or Golang is a plus
- Hands-on with agent orchestration and tool/function calling; ability to design planning/execution loops and multi-agent workflows
- Strong RAG expertise, including document ingestion, chunking, embeddings, indexing, vector databases, retrieval tuning, and grounded responses with citations.
- Experience with AI frameworks and platforms such as LangGraph, LangChain, AutoGen, CrewAI, OpenAI APIs, pgvector, or Pinecone.
- Solid API engineering fundamentals, including authentication, authorization, input validation, error handling, versioning, rate limiting, retries, timeouts, and budget controls.
- Ability to implement safety, compliance, evaluation, and observability practices, including PII redaction, content moderation, audit logging, RBAC, traces, metrics, logs, and quality benchmarks.
- Production engineering mindset, including testing, CI/CD, documentation, runbooks, staged rollouts, incident response support, and collaboration with backend, QA, Product, and Design teams.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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