AI Engineer

Position Overview

We are seeking a Senior AI Solution Engineer to lead the design, deployment, and integration of our AI solutions into customer environments. Reporting directly to the CTO, you will act as the key technical AI implementation person, facing our enterprise clients. You will play a central role in interpreting customer business requirements and translating them into scalable, secure, and maintainable cloud-based AI architectures — with a particular focus on AWS deployments of TrueAgent and TrueRAG.

Key Responsibilities

• Work directly with clients to understand their business goals, technical needs, and integration requirements.
• Architect and implement hands-on end-to-end AI solutions on AWS, including infrastructure, security, data pipelines, model orchestration, and monitoring, under the auspices of the CTO and utilising our TrueAgent and TrueRAG solutions as the main backbone.
• Customize and deploy TrueAgent and TrueRAG for a variety of use cases such as customer service automation, knowledge retrieval, and process optimization.
• Ensure solutions meet enterprise-grade security, compliance, and performance standards.
• Provide post-deployment support and guidance to customers during the project and for a period afterwards to ensure adoption.
• Document architecture, decisions, and reusable assets to accelerate future deployments.
  
  

Required Qualifications

• 5+ years of experience in cloud solution architecture, preferably in a consulting or enterprise customer-facing role.
• Proven experience designing and implementing AI/ML solutions on AWS, including services such as SageMaker, Bedrock, Lambda, API Gateway, ECS, S3, IAM, Step Functions and CloudFormation or Terraform.
• Solid understanding of LLMs, Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, FAISS), hierarchical object databases (eg Neo4G) and agentic frameworks, such as LangGraph.
• Proficiency in Python and familiarity with Rust, Go.
• Experience integrating with ML libraries and APIs (eg Microsoft GraphQL.).
• Familiarity with CI/CD pipelines, containerization (Docker), and infrastructure-as-code.
• Strong communication and interpersonal skills — able to translate technical concepts for non-technical stakeholders.
• Ability to code effectively through the use of coding assistants, such as Claude Code, OpenAI Codex or Amazon Q.
  
  

Preferred Qualifications

• AWS Certified Solutions Architect (Professional or Associate).
• Experience with AI governance, model evaluation, and prompt engineering best practices.
• Prior experience deploying enterprise AI/LLM solutions in the Energy or Telecoms sector
• Experience with data integration, ETL pipelines, and API development.
• Background in enterprise software development and systems integration.