Machine Learning Engineer - Hybrid Remote

AmpsTek – a global technology leader since 2013 – is transforming how businesses approach technology and staffing solutions. Founded by seasoned technology leaders across the UK, Europe, APAC, North America, and LATAM, and with registered offices in 30+ countries, we deliver exceptional service, scalable solutions, and measurable impact. With a portfolio of 200+ clients and millions of users across web and mobile platforms, we empower businesses to innovate, grow, and succeed. Join our team and be part of a dynamic, growth-oriented organization that values talent, creativity, and results.

Role: ML Engineer

Location: London, UK (Hybrid 3 days/week)

Contract (Inside IR35)

• AI Model design and build: Work closely with data scientists and business to design and implement AI algorithms, frameworks and architectures.
• AI model Data Preprocessing: Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and load data from various sources.
• AI model Feature Engineering: Integrate structured and unstructured data from internal and external systems into centralized data platforms.
• Performance Tuning of AI/models: Optimize data workflows and queries for performance, scalability, and cost-efficiency.
• Building Agentic Systems: Developing intelligent AI agents that can reason, plan, and execute tasks autonomously using LLMs and other tools.
• LLM application Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques like parameter-efficient fine-tuning (PEFT).
• Responsible AI: Build AI systems which are trustworthy and beneficial considering ethical principles such as fairness, transparency, accountability, privacy and reliability.
• AI Model Deployment and Lifecycle Management: Orchestrate robust and error-free deployment of AI models into production environments, making them accessible to applications and users.
• Automation and Pipeline Management: Create and manage automated pipelines for AI workflows including training, testing and deployment.
• Monitoring and Maintenance: Set up monitoring systems to track key metrics such as prediction accuracy, response times, resource utilization, and error rates of deployed models.
• Infrastructure Management: Manage the infrastructure required for training, testing, and running AI models in production, including provisioning hardware and software resources, leveraging cloud platforms and containerization technologies like Docker and Kubernetes.
• Data and Model Versioning and Rollback: Implement version control for data and models, allowing for tracking changes, testing older versions, and ensuring reproducibility.
• Collaboration and Communication: Collaborate extensively with data scientists, software engineers, and DevOps teams to ensure smooth integration of AI models.