AI Engineer

In this role, you will design, build, and deploy Generative AI solutions—LLM-powered applications, retrieval-augmented generation (RAG), and agentic workflows—integrated with enterprise data and systems. Working alongside senior engineers and in close collaboration with product, engineering, and security partners, you'll help deliver reliable, scalable, and safe GenAI capabilities, with a focus on evaluation, observability, and cost/latency performance in production.

Responsibilities:

• Design and implement LLM-powered conversational experiences (chat assistants/AI Agents), integrating tools, APIs, and enterprise data sources.
• Build and maintain RAG pipelines (document ingestion, chunking, embeddings, indexing) using vector databases and AI-enabled search.
• Develop LLM inference and orchestration components (prompt templates, function/tool calling, streaming, caching) that meet defined latency and reliability targets. Implement safety and compliance guardrails (content filtering, prompt injection defences, data access controls) and support red-teaming/testing of GenAI applications.
• Help optimise GenAI solutions for cost and performance (token usage, prompt compression, caching, model routing) while maintaining response quality.
• Apply Responsible AI practices for GenAI (grounding, hallucination mitigation, privacy-by-design, bias/toxicity evaluation) and align with security requirements.
• Document designs, prompts, evaluation results, and operational runbooks; contribute reusable components and code that the wider team can build on.

Requirements:

• BE/BTech in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience).
• 3-5 years of total experience as a software engineer, including hands-on experience building and shipping AI/ML and GenAI solutions (LLMs, RAG, prompt/tool calling) using Python. Good to have: Node, React, Terraform, Git, GitLab, Docker, Kubernetes.
• Programming skills in Python and experience building LLM applications using common frameworks (e. g., LangChain, LangGraph); familiarity with evaluation/testing frameworks is a plus.
• Hands-on experience deploying GenAI applications on cloud platforms (GCP/Azure/AWS), including integration with managed LLM services and secure API patterns.
• Experience with vector databases and retrieval systems (e. g., Pinecone, Milvus, Weaviate, Elasticsearch/OpenSearch); exposure to LLMOps tooling for prompt/version management and evaluation is a plus.
• Working knowledge of CI/CD and familiarity with observability for GenAI systems (quality/safety metrics, tracing, monitoring latency and cost).
• Working knowledge of Agile delivery and collaboration tools (e. g., Jira, Confluence); ability to clearly communicate technical work and trade-offs to the team.
• Strong analytical and problem-solving skills to debug GenAI issues (grounding gaps, hallucinations, prompt injection, retrieval quality) across application, data, and infrastructure layers.