Role Summary

We are seeking a Lead Engineer, AI Systems to serve as the technical anchor across our AI-augmented engineering organisation. This role bridges our Senior AI Coding Engineers, the AI/ML Developer (SLM Specialist), and the Agentic Systems intern cohort. You will set technical direction, own cross-cutting architectural decisions, and champion the responsible, high-impact adoption of AI-assisted development practices across the entire engineering function.

You will remain hands-on with code while simultaneously shaping how the wider team builds, evaluates, and ships AI-powered products.

Key Responsibilities

1  Technical Vision & Architecture

• Define and own the end-to-end technical architecture for AI Systems — spanning product feature surfaces, model inference APIs, and agentic toolchains.
• Drive Architecture Decision Records (ADRs), system design reviews, and RFC processes across squads.
• Establish standards for integrating SLM/LLM model endpoints into product surfaces built by the Senior Engineering team.
• Evaluate emerging AI infrastructure patterns (RAG, agentic orchestration, vector stores, model serving) and guide adoption decisions.
• Own the technical roadmap for AI tooling, developer productivity, and model integration layers.

2  Cross-Squad Technical Leadership

• Act as the primary technical liaison between the AI/ML Developer (model side) and Senior Engineers (product side), ensuring smooth API contracts, evaluation loops, and deployment handoffs.
• Provide technical direction to Agentic Systems interns, reviewing designs, code, and agentic pipeline implementations.
• Unblock senior engineers on hard architectural or integration challenges that span squad boundaries.
• Run cross-team design reviews, architecture syncs, and engineering guild sessions.

3  AI-Augmented Engineering Excellence

• Define and maintain the organisation’s standards for AI-assisted development — covering context engineering, AI code review protocols, context management, and tool evaluation criteria.
• Maintain and evolve the internal AI tooling playbook (Cursor IDE, Claude Code, Codex CLI, and emerging tools).
• Evaluate new AI coding tools, agentic frameworks (LangChain, LlamaIndex, CrewAI, AutoGen), and developer-productivity platforms; produce adoption recommendations with measured trade-offs.
• Conduct structured audits of AI-generated code across squads for correctness, security, and maintainability.

4  Hands-On Engineering

• Remain an active contributor: own critical-path features, prototype architectural spikes, and build shared infrastructure components used across squads.
• Personally drive resolution of the most complex production incidents and root-cause analyses.
• Review and merge high-impact PRs; maintain the highest code review quality bar on the team.
• Own observability and reliability for AI inference and MLOps integration layers in production.

5  Mentoring & Talent Development

• Mentor Senior Engineers, the AI/ML Developer, and interns through technical coaching, design feedback, and stretch assignments.
• Lead hiring panels and technical interviews; help define and uphold the engineering hiring bar.
• Contribute to onboarding frameworks that embed AI-first practices from day one.
• Model a culture of rigorous experimentation, psychological safety, and continuous improvement.

6  Stakeholder & Product Collaboration

• Partner with the Director of Engineering and Product leadership to translate product strategy into a phased technical roadmap.
• Present architectural proposals and trade-off analyses to engineering leadership and executive stakeholders as required.
• Coordinate with DevOps/Platform teams on GPU/TPU compute provisioning, CI/CD for model pipelines, and cloud cost optimisation.

Required Qualifications

1  Education

• Bachelor’s or Master’s degree in Computer Science, or a related field.
• Candidates without a degree but with a compelling portfolio demonstrating scope and impact at staff/lead level will be considered.

2  Experience

• 6 – 8 years of professional software engineering experience in product-focused environments.
• Minimum 2 years in a formal or de-facto technical lead / staff engineer capacity across multiple squads or systems.
• Minimum 8-12 months of active, hands-on experience with AI coding tools in a professional engineering setting.
• Proven track record of shipping production systems with measurable business impact at scale.
• Demonstrated experience collaborating closely with ML/AI model teams on integration, deployment, and evaluation.

3  Technical Skills — Core Engineering

• Languages: Expert proficiency in at least two of — Python, TypeScript/JavaScript, Go.
• Architecture: Microservices, event-driven systems, API design (REST, GraphQL, gRPC), distributed systems fundamentals.
• Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis); vector databases (FAISS, Pinecone, Weaviate).
• Cloud: AWS, GCP, or Azure — compute, storage, serverless, managed ML services (SageMaker, Vertex AI).
• DevOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins); IaC (Terraform/Pulumi); observability stacks.
• Testing: TDD/BDD; unit, integration, and e2e frameworks; model evaluation pipelines.

4  AI / ML Integration Skills (Mandatory)

• Demonstrated proficiency with AI coding assistants (Cursor IDE, Claude Code, Codex CLI) in daily professional workflows.
• Experience designing and consuming LLM/SLM inference APIs; understanding of model serving, latency, and cost trade-offs.
• Hands-on familiarity with RAG architectures, vector stores, and retrieval pipelines.
• Ability to define and enforce AI code quality standards across an engineering team.
• Understanding of LLM limitations: hallucinations, context window constraints, prompt injection risks, and licensing considerations.

5  Technical Skills — Nice to Have

• Experience with SLM/LLM training or fine-tuning pipelines (SFT, RLHF, LoRA/QLoRA).
• Familiarity with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) and multi-step reasoning pipelines.
• Contributions to open-source ML or developer tooling projects.
• Knowledge of OWASP Top 10, secure coding, and AI-specific security risks (prompt injection, model exfiltration).
• Prior experience in a high-growth startup or scale-up environment with rapid iteration cycles.

Behavioural Competencies

• AI-First Mindset: Defaults to AI tools to accelerate work while maintaining rigorous quality standards; actively pushes the team’s AI capability forward.
• Systems Thinking: Sees the full picture — how model outputs, product surfaces, and infrastructure interdepend; anticipates second-order effects of architectural choices.
• Ownership: Takes end-to-end responsibility from design through production; doesn’t hand off problems, solves them.
• Influence Without Authority: Earns trust through technical credibility; drives alignment through persuasion, data, and well-reasoned proposals.
• Communication: Writes clear design docs, ADRs, and post-mortems; articulates complex trade-offs for engineering peers and non-technical stakeholders.
• Mentorship: Actively invests in the growth of engineers at all levels; gives direct, constructive feedback.
• Pragmatism: Balances the ideal with the shipped; knows when to iterate vs. refactor vs. rewrite.
• Curiosity: Continuously explores emerging research, tools, and patterns — and brings those learnings back to the team.

Role Summary

We are looking for a technically strong AI / ML Developer with hands-on expertise in training and fine-tuning Small Language Models (SLMs) and RAG Solutions. The ideal candidate will drive end-to-end AI Solutions — from dataset curation and pre-processing to training, evaluation, and production deployment. You will collaborate closely with product, product engineers, and infrastructure teams to build AI solutions that are efficient, scalable, and business-aligned.

Key Responsibilities

1  Model Design & Training

• Design, train, and fine-tune Small Language Models (SLMs) using frameworks such as PyTorch, TensorFlow, or JAX.
• Conduct experiments with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning.
• Implement efficient training pipelines leveraging distributed training (DDP, FSDP) across GPU/TPU clusters.
• Perform hyperparameter optimisation, ablation studies, and model selection based on benchmark results.
• Develop and maintain data pipelines for collecting, cleaning, tokenising, and pre-processing large-scale training corpora.

2  Model Evaluation & Quality

• Define and implement evaluation frameworks including perplexity, BLEU, ROUGE, BERTScore, and task-specific benchmarks.
• Conduct red-teaming, bias analysis, and safety evaluations to ensure responsible AI deployment.
• Benchmark models against established baselines (e.g., GPT-2, Phi, Mistral) and track performance over iterations.
• Collaborate with QA teams to build regression suites for model versioning and continuous evaluation.

3  MLOps & Deployment

• Containerise and deploy models using Docker, Kubernetes, and cloud-native ML platforms (AWS SageMaker / GCP Vertex AI / Azure ML).
• Build and maintain model registries, experiment tracking (MLflow, Comet), and reproducible training pipelines.
• Optimise inference performance through quantisation (INT8, INT4), pruning, distillation, and ONNX/TensorRT conversion.
• Monitor model drift, data drift, and performance degradation in production; implement automated retraining triggers.

4  Research & Innovation

• Stay current with state-of-the-art NLP/LLM research; prototype and validate new techniques from published literature.
• Contribute to internal knowledge sharing, technical documentation, and model cards.
• Explore parameter-efficient fine-tuning (PEFT) methods such as LoRA, QLoRA, and Adapter layers.
• Investigate and apply mixture-of-experts (MoE), retrieval-augmented generation (RAG), and agentic workflows as needed.

5  Collaboration & Stakeholder Management

• Work with product managers and domain experts to translate business requirements into model objectives and KPIs.
• Communicate model capabilities, limitations, and trade-offs clearly to both technical and non-technical stakeholders.
• Participate in architecture reviews, sprint planning, and cross-functional design discussions.

Required Qualifications

1  Education

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related quantitative field.
• Equivalent professional experience with a strong portfolio of delivered AI/ML projects will be considered.

2  Experience

• 4 – 6 years of professional experience in machine learning or NLP engineering.
• Minimum 2 years of direct, hands-on experience in training or fine-tuning language models (LLMs or SLMs).
• Demonstrable experience taking a model from dataset preparation through to production deployment.

3  Technical Skills — Core

• Programming: Python (proficient); familiarity with C++/CUDA a plus.
• Deep Learning Frameworks: PyTorch (primary), TensorFlow or JAX.
• NLP Libraries: Hugging Face Transformers, Datasets, PEFT, TRL, Accelerate.
• Training Infrastructure: Multi-GPU training, FSDP, DeepSpeed ZeRO stages 1–3.
• Data Engineering: Pandas, NumPy, Apache Spark or Dask for large-scale data prep.
• Vector DBs & Retrieval: FAISS, Pinecone, Weaviate, or equivalent.
• Cloud Platforms: AWS, GCP, or Azure — ML-specific services (SageMaker, Vertex AI, etc.).
• Version Control & CI/CD: Git, GitHub/GitLab, MLflow or W&B for experiment tracking.

4  Technical Skills — Nice to Have

• Experience with multimodal models or vision-language models (VLMs).
• Knowledge of model safety, alignment techniques, and responsible AI frameworks.
• Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks.
• Contributions to open-source ML projects or publications at NeurIPS, ICML, EMNLP, ACL.

Behavioural Competencies

• Intellectual Curiosity: Keeps up with rapidly evolving AI research and applies learnings pragmatically.
• Problem Solving: Approaches ambiguous problems with structured, data-driven thinking.
• Ownership: Takes full accountability for model quality, timelines, and production reliability.
• Collaboration: Works effectively in cross-functional teams with diverse skill sets.
• Communication: Distils complex technical concepts for diverse audiences.
• Adaptability: Thrives in a fast-paced environment where priorities evolve with research breakthroughs.

Tesco Media & Insight Platform is a partnership between Tesco, the UK’s largest grocery retailer, and dunnhumby. Fueled by first‑party behavioral data from over 23 million Clubcard holders, we help brands understand consumer behaviors and reach the right customers, at the right time, with the right message across the full marketing funnel. Our closed‑loop measurement connects what customers see with what they buy, helping brands Shape What Britain Buys and maximize the impact of every pound of media investment.

Roles and Responsibilities 

As a Senior AI Engineer, you will be a hands‑on technical contributor designing, building and operating production‑grade GenAI and agentic AI solutions that power Tesco Media & Insight Platform’s global products and services. You will work closely with the AI Engineering Manager, Product, Data Science and Engineering teams to turn business problems into secure, scalable, observable AI systems that deliver measurable value for brands and shoppers.

• Design and implement GenAI and agentic AI solutions on GCP, leveraging Vertex AI (models, endpoints, pipelines, vector search/RAG, Agent Builder) and core GCP services.
• Build AI agents and multi‑step agentic workflows, including tool calling, orchestration, and integrations with internal and external systems.
• Contribute to the design of MCP servers and agent‑to‑agent / agent‑to‑application protocols to enable safe, reliable automation of retail media and insight use cases.
• Work with the AI Engineering Manager and architects to define solution designs, interface contracts and non‑functional requirements (security, reliability, performance, cost).
• Implement security, privacy and governance controls in AI solutions, including safe prompt and tool design, data protection, secrets management and access control.
• Apply DevOps/MLOps practices: contribute to CI/CD pipelines for models, prompts and agents; use infrastructure as code; support environment management and automated testing.
• Implement observability for AI systems: instrument prompts, model calls, tools and agents with tracing, metrics and logs; build and maintain dashboards and alerts.
• Help define and run AI testing and evaluation strategies, including automated tests, offline and online evaluation, red‑teaming, regression suites and human‑in‑the‑loop review flows.
• Collaborate with Retail Media and Insight teams to understand use cases and data, and translate requirements into concrete AI components and services.
• Write high‑quality, well‑tested code in Python (and, where relevant, TypeScript/Java or similar) following team standards for readability, performance and reliability.
• Participate in code reviews, design reviews and incident post‑mortems, and contribute to continuous improvement of our AI engineering practices.
• Stay current on advances in LLMs, agentic frameworks and cloud AI services, and bring forward ideas for how they can be applied to Tesco Media & Insight Platform.

What you’ll need to succeed in the role

• Strong experience in software/data/ML engineering, with hands‑on delivery of AI/ML or LLM‑based solutions into production (not just prototypes).
• Practical experience on GCP and Vertex AI, including deploying models, building pipelines, and implementing RAG/vector search‑based solutions.
• Solid understanding of LLMs and foundation models: transformers at a high level, tokenization, embeddings, context windows, RAG, and the trade‑offs between fine‑tuning, adapters and prompt‑based approaches.
• Hands‑on experience building agentic workflows or AI agents (e.g. tool‑augmented chat, multi‑step workflows, or multi‑agent systems) using one or more modern frameworks or platforms.
• Good grasp of security and safety for AI systems, including prompt and tool design to reduce hallucinations and prompt injection, safe handling of sensitive data, and application‑level guardrails.
• Experience with DevOps/MLOps practices: CI/CD, version control, automated testing, infrastructure as code, monitoring and incident handling in a cloud environment.
• Strong programming skills in Python and experience building and consuming APIs; familiarity with microservices and event‑driven architectures is a plus.
• Ability to design and run evaluation and experimentation for AI features, including quantitative and qualitative measures and, where appropriate, human‑in‑the‑loop review.
• Strong problem‑solving skills, attention to detail, and a willingness to take ownership of complex technical challenges.
• Clear, proactive communication and the ability to collaborate effectively with product managers, data scientists, engineers and commercial stakeholders across locations.