AI Infrastructure Architect

Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimise AI infrastructure/hardware performance, power consumption, cost and scalability of the computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full-stack integration.

Responsibilities:

• Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines and evaluation harnesses.
• Design agent workflows using Bedrock and serverless AWS patterns; integrate enterprise APIs through Lambda and API Gateway; build secure RAG over S3 OpenSearch and Knowledge Bases; tune prompts and evaluation test suites for accuracy, relevance, faithfulness and safety.
• Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search and retrieval workflows for structured and unstructured enterprise content.
• Engineer safety and control components including PII detection/redaction, prompt-injection defences, content filters, guardrails, authentication, authorisation, lineage and audit logging.
• Collaborate with architects, data engineers, product owners and security stakeholders to convert solution designs into tested, observable and maintainable software components.
• Maintain technical artefacts such as component designs, integration specifications, deployment runbooks, evaluation results and reusable engineering patterns.

Requirements:

• Bachelor's degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
• Hands-on coding experience in Python and strong understanding of APIs, distributed systems, CI/CD, testing, observability and secure SDLC practices.
• Experience delivering AI/ML or data products in at least one industry domain such as banking, insurance, healthcare, retail, telecom or capital markets.
• Hands-on experience with Amazon Bedrock, Bedrock Agents/AgentCore, Knowledge Bases, Guardrails, Lambda, API Gateway, Step Functions, OpenSearch Serverless/Vector Engine, SageMaker, IAM, CloudWatch, CloudTrail, VPC, KMS, S3
• Strong understanding of LLM application architecture patterns including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases and evaluation metrics.
• Ability to implement traditional ML and GenAI components across ingestion, feature/data preparation, model integration, deployment, monitoring and continuous improvement.
• Practical knowledge of security, privacy, governance, performance, scalability, reliability and cost controls for production AI systems.
• Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code and agile delivery in client-facing environments.

Good to Have Skills:

• AWS Solutions Architect or Machine Learning Speciality certification experience with CDK/Terraform, EKS, Bedrock model evaluation, Amazon Q, responsible AI controls and FinOps for GenAI workloads.
• Exposure to open-source frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker and Kubernetes.
• Experience with Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing and GenAI cost optimisation.
• 15 years of full-time education.