AI Engineer – Agentic AI (Training & Enablement)
Who we are:We deliver powerful data and AI solutions that minimize operational costs, strengthen resilience against risk, and uncover revenue opportunities. Clients can retain expert teams to drive lasting adoption while futureproofing their workforce with exceptional talent. Since 2016, more than 3,000 data & AI specialists have been created by removing systemic barriers to the tech industry. Incredible minds from all backgrounds are trained to become part of a diverse team of experts. Work spans a broad range of industries, including financial services, insurance, asset management, pharmaceuticals, energy and natural resources, retail, healthcare, manufacturing, and mobility. As a preferred partner of today’s leading technology providers - such as Databricks, Snowflake and Collibra - delivery As an AI Engineer (Agentic AI) within our AI Engineering Training & Enablement capability, you will play a hands‑on role in developing, testing, and evolving agentic AI systems while contributing to the development and delivery of high‑quality technical training. This is a learning‑focused engineering role, combining practical AI engineering with the ability to contribute and deliver training labs, exercises, and technical artefacts that underpin our training offer. You will act as a technical contributor and subject matter specialist, supporting trainers, learners, and—where appropriate—commercial or client‑facing teams by demonstrating the real‑world credibility of our AI engineering practices.
Key Responsibilities
- Agentic AI Engineering & Practical Enablement
- Design, build, and maintain diverse AI systems, ranging from classical machine learning pipelines to advanced agentic AI architectures (tool‑using agents, RAG, orchestration).
- Develop production‑ready reference implementations that demonstrate best practices in software engineering, including testing, version control, and CI/CD for AI applications.
- Create and maintain robust engineering environments, including containerised setups and cloud infrastructure, to support technical enablement and experimentation.
- Act as a subject matter expert on the full AI lifecycle, providing technical guidance on data preparation, model training, MLOps, and deployment strategies.
- Experiment with new agentic patterns, tools, and frameworks and translate learnings into practical training artefacts.
- Training Delivery & Learner Support
- Lead technical sessions and workshops covering the full spectrum of AI engineering, from foundational Python and statistics to deep learning and Generative AI.
- Mentor junior engineers and consultants, helping them debug complex issues across data pipelines, infrastructure configuration, and model development.
- Continuously refine technical artefacts and exercises to ensure they reflect current industry standards and realistic enterprise challenges.
- Engineering Standards, Evaluation & Responsible AI
- Embed evaluation practices into agentic AI builds, including benchmarking, regression testing, and failure mode inspection.
- Contribute to responsible AI patterns within training content (data boundaries, permissions, guardrails).
- Ensure training artefacts reflect modern engineering standards and realistic enterprise constraints.
- Collaboration & Capability Development
- Work closely with trainers, architects, and senior engineers to align training content with best practice.
- Contribute to internal policy and standards documentation, knowledge sharing, and centres of excellence.
- Support onboarding and enablement of new trainers or engineers.
- Lead internal demos, showcases, or events highlighting agentic AI capability.
Key Requirements
- Experience
- 3–6+ years in software engineering, Data Science, or Machine Learning Engineering roles, with a track record of building and deploying production systems.
- Technical Skills
- Core Engineering: Expert proficiency in Python (writing modular, production‑quality code) and strong working knowledge of SQL and database design.
- Maths & Statistics: Strong grasp of probability theory, statistical analysis, and linear algebra, with the ability to apply these concepts to real‑world data problems.
- Classical Machine Learning: Deep practical knowledge of standard ML algorithms (regression, classification, clustering, ensemble methods) and libraries (scikit‑learn, pandas).
- Infrastructure & Containers: Hands‑on experience with containerisation (Docker) and orchestration (Kubernetes), including how to package and run AI applications in these environments.
- MLOps & Deployment: Proficiency with MLOps frameworks (specifically MLflow) for experiment tracking and model lifecycle management, as well as experience deploying models as APIs (FastAPI/Flask).
- Generative AI: Experience building applications with LLMs, including prompt engineering, RAG architectures, and vector databases.
- Rapid Prototyping: Proficiency in guiding AI coding assistants to rapidly generate and refine functional web interfaces (e.g., Streamlit, React) for agentic demos.
- Cloud Platforms: Proven experience developing and deploying AI solutions on Azure (specifically Azure ML and Cognitive Services) or equivalent AWS/GCP services.
- DevOps: Familiarity with modern DevOps practices, including Git‑based workflows and CI/CD pipelines (e.g., GitHub Actions, Azure DevOps).
- Agile Product Delivery: Familiarity with agile product delivery and requirements engineering practices.
- Ways of Working
- A genuine passion for upskilling others, with a patient and collaborative approach to code reviews and technical problem‑solving.
- Exceptional ability to explain complex engineering and mathematical concepts to audiences with varying levels of technical expertise.
- Collaborative approach suited to a team‑oriented, learning‑focused environment.
- Self‑directed and proactive – the ability to navigate uncertainty and ambiguity with business and technical stakeholders to co‑create value.
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