Overview

As discussed, please share the candidate profile with high priority today itself.

Focus on the candidate's skill set, communication abilities, and overall acumen. Experience - 8+ years.

Note: The selected candidate's internal interview will be conducted today if the profile is selected, so pls ensure the candidate is available since we have an approval.

Key Responsibilities

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 the delivery lifecycle from prototype to production reliability.

• 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, safe 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.

Required Skills

• 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.

Qualifications

• At least 5 years of software engineering experience, preferably with hands-on delivery of AI or LLM systems in production.
• Strong full-stack development experience with a good understanding of UI/UX principles.
• Strong proficiency in Node.js, TypeScript, and React.
• Working knowledge of Python for AI/LLM integrations and exposure to Golang are pluses.
• Solid API engineering fundamentals, including authentication, authorisation, input validation, error handling, versioning, integration design, and secure system development.
• Strong testing and delivery practices, including unit, integration, and end-to-end testing, Agile collaboration, code reviews, production rollouts, and writing clean, maintainable, production-ready code.