We are hiring an AI Systems Engineer to design, build, and deploy end-to-end AI systems across the organisation—ranging from client-facing AI products to internal tools supporting data science, product, engineering, and revenue teams—on top of robust, scalable AWS infrastructure.

This is a hands-on role spanning AI system design, agent architectures, LLM engineering, cloud deployment, and AIOps/MLOps for production reliability. You will work on LLM applications, agentic workflows, RAG systems, ML pipelines, analytics automation, and microservices.

Key Responsibilities

•         Design, build, and operate end-to-end AI/LLM systems, including chatbots, analytics assistants, automation tools, and decision-support services.

•         Develop internal productivity and intelligence tools that accelerate workflows across data science, product, engineering, and revenue teams.

•         Build autonomous AI agents and workflow orchestrators using frameworks such as LangChain, CrewAI, ADK, or equivalent systems.

•         Design and implement LLM-backed microservices (FastAPI/Flask) for summarisation, intelligence, forecasting, data extraction, and API-driven reasoning.

•         Build and operate full Retrieval-Augmented Generation (RAG) pipelines: ingestion → chunking → embeddings → indexing → retrieval → LLM reasoning.

•         Optimise retrieval quality using metadata, hybrid search, chunking strategies, rerankers, and relevance tuning.

•         Implement document classification, NER, entity extraction, and knowledge-graph-driven retrieval where appropriate.

•         Establish reliability, safety, and governance guardrails across AI systems, including monitoring, error handling, tool-selection controls, and risk mitigation.

•         Instrument, monitor, and evaluate AI and RAG systems using logging, metrics, tracing, agent telemetry, quality benchmarks, hallucination testing, and regression tests.

•         Deploy and operate AI agents and LLM microservices on AWS (Bedrock, Lambda, ECS/EKS, API Gateway, S3, Secrets Manager, CloudWatch).

•         Build and maintain production CI/CD pipelines (GitHub Actions), manage model/version lifecycles, and support retraining and automated evaluation workflows.

Required Skills & Qualifications

Key Skills

• Strong software engineering background with expertise in Python, including modular design, async programming, and modern development practices.

• Experience designing and building APIs and microservices (FastAPI / Flask) for production systems.

• Hands-on experience building and operating production LLM systems, including agentic workflows and RAG pipelines.

• Experience designing and operating RAG systems, including vector databases and retrieval pipelines.

• Experience designing and running LLM evaluations, including task-level metrics, hallucination testing, regression benchmarks, and golden datasets.

• Hands-on familiarity with one or more of LLM observability and evaluation tooling such as OpenTelemetry, LangSmith, Weights & Biases, Arize/Phoenix, or equivalent in-house systems.

• Experience deploying and operating AI systems on AWS (Bedrock, EC2, Lambda, ECS/EKS, API Gateway, S3, CloudWatch), with a focus on reliability, security, and cost-aware production usage.

Nice-to-Haves

• Familiarity with Docker, Kubernetes, CI/CD, and continuous deployment in production environments.

• Experience with search and retrieval systems such as AWS Kendra, OpenSearch, Weaviate, Qdrant, or Pinecone.

• Ability to build simple internal-facing UIs or tools (React, Streamlit).

• Experience building reusable SDKs, internal AI platforms, or shared developer frameworks.

Who You Are

• You have 5+ years of overall experience in software engineering/ML engineering, with at least 2 years building GenAI systems in production.

• You ship real production systems, not just prototypes.

• You operate at the intersection of AI, engineering, and operations.

• You think in systems: reliability, observability, cost, and scale.

• You work independently, own problems end-to-end, and simplify complexity.

• You prioritise safety, interpretability, and security in every AI system you build.