AI Engineer (AWS / Generative AI)

The Role

• Building AI solutions is one thing. Getting them into production, making them work reliably and delivering real value to a business is another.
• We’re looking for an experienced AI Engineer who enjoys doing both.
• You’ll join an established AWS cloud services business, working with clients across different industries to design, build and deploy practical AI, machine learning and Generative AI solutions.
• This is a hands-on engineering role with a strong client-facing element. You’ll be involved from the first discovery conversation through solution design, development, deployment and ongoing improvement.
• You won’t be sitting on the sidelines drawing up concepts for someone else to build. You’ll write production-quality code, make technical decisions and take ownership of delivery.
• There’s also room to grow, whether your future lies in senior technical leadership or leading projects and engineers.
• Core Tech: Python, AWS, Amazon Bedrock, Amazon SageMaker, AWS Lambda, APIs, Terraform, CI/CD, GenAI, RAG and Agentic AI.

Responsibilities

• Design, develop, test and deploy AI, ML and Generative AI solutions using AWS technologies.
• Build GenAI applications using Amazon Bedrock, including prompt workflows, embeddings, retrieval-based solutions and AI agents.
• Develop and deploy machine learning pipelines using Amazon SageMaker.
• Write clean, maintainable Python code and build APIs to integrate AI capabilities into business applications.
• Develop and maintain infrastructure as code to support reliable deployments.
• Participate in client discovery sessions, understand business challenges and identify where AI can deliver measurable value.
• Progress towards independently leading technical assessments and client workshops.
• Evaluate solution options, technical feasibility, costs and potential risks.
• Help translate client requirements into practical project plans, estimates and deliverables.
• Take ownership of assigned projects from development through deployment, monitoring and optimisation.
• Work closely with engineers and delivery teams to resolve technical challenges and maintain delivery standards.
• Contribute to reusable solutions, technical documentation and engineering best practices.
• Share knowledge and support other engineers as the team grows.

Requirements

• We’re looking for someone who has moved beyond experimenting with AI and has practical experience building solutions that work in real environments.

Essential experience:

• 3+ years of hands-on experience in AI engineering, applied machine learning or software development with substantial practical AI exposure.
• Strong Python development skills, including writing production-quality applications.
• Practical experience implementing machine learning solutions using Amazon SageMaker.
• At least 1 year of hands-on experience with Amazon Bedrock, including retrieval-based solutions and agentic workflows.
• Experience taking at least one ML or GenAI solution from experimentation into production or near-production, including post-deployment support.
• Sound understanding of ML fundamentals, including data preparation, feature engineering, model selection, evaluation and monitoring.
• Experience developing and integrating APIs for AI and ML applications.
• Experience with AWS Lambda, particularly Python-based workloads.
• Familiarity with AWS services supporting knowledge bases and retrieval solutions.
• Experience using Terraform or similar infrastructure-as-code tools.
• Good working knowledge of Git, CI/CD pipelines and Linux environments.
• Ability to explain technical solutions, limitations and trade-offs to both technical and non-technical stakeholders.
• Confidence engaging directly with clients, understanding their requirements and contributing to technical discovery discussions.
• Ability to manage competing priorities and take ownership of deliverables.

Advantageous experience:

• Leading AI/ML client assessments, workshops and technical discovery engagements.
• Experience in consulting, technical pre-sales, solution scoping or preparing project estimates.
• Exposure to multiple client projects or industry environments.
• Amazon Bedrock AgentCore, Strands, MCP integrations or other agentic AI frameworks.
• Advanced SageMaker capabilities, including model tuning, pipelines, feature stores and model monitoring.
• Vector databases and retrieval technologies such as Amazon OpenSearch, PostgreSQL with pgvector, S3 Vectors or Amazon RDS.
• Containerised deployments using Amazon ECS or EKS.
• AWS AI services such as Rekognition, Transcribe, Polly or Comprehend.
• Golang development experience.
• AWS certifications in Machine Learning or Generative AI.
• Bachelor’s degree in Computer Science, Engineering, IT or a related field.