AI Engineer – London, UK (Hybrid) – Contract
We are hiring an AI Engineer who will build intelligent systems and AI‑powered capabilities that enable customers in fast‑moving, data‑rich industries to operate, scale, and innovate. You will develop robust, production‑ready AI solutions that harness automation, advanced analytics, and machine learning to power real‑time decision‑making across complex digital transformation programmes. With access to cutting‑edge AI frameworks, high‑performance compute, and modern data platforms, you will work closely with architects and data scientists to engineer secure, scalable, and ethical AI applications. This role empowers you to bring end‑to‑end AI ecosystems to life—accelerating delivery, enhancing customer experiences, strengthening operational resilience, and helping organisations realise the full potential of an AI‑enabled future.
Responsibilities
* Build and ship production‑ready AI/ML features—from data ingestion and feature engineering to model training, evaluation, and deployment.
* Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, re‑ranking).
* Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/TPU), and efficient memory/latency tuning.
* Build and maintain MLOps/LLMOps workflows—CI/CD for models and prompts, model registry/versioning, feature stores, and automated promotion across environments.
* Instrument observability for data, models, and prompts (telemetry, metrics, traces, dashboards, alerts); implement A/B tests and online/offline evaluation.
* Embed Responsible AI considerations (fairness, explainability, safety, bias testing) and document assumptions, datasets, and limitations.
* Document architecture, workflows, and best practices to support scalability and ongoing maintainability.
* Conduct code reviews, write unit/integration/e2e tests (including data and prompt tests), and uphold engineering standards and documentation.
* Work with advanced AI/ML frameworks, cloud services, and container orchestration platforms.
* Partner closely with data scientists to productionise models and integrate them seamlessly into applications and enterprise workflows.
Qualifications
Essential skills/knowledge/experience
* 5–12 years experience as an AI Engineer.
* Hands‑on experience with GenAI and large language models (Gemini or open‑source LLMs) – training, fine‑tuning, and onboarding new LLMs.
* Experience building GenAI applications with Python.
* Hands‑on experience with API development and microservices architecture/end‑to‑end integrations.
* Knowledge of RAG (Retrieval‑Augmented Generation) and related tools.
* Solid understanding of LLMs, prompt engineering, and graph‑based workflows.
* Experience with CI/CD pipelines, containerisation (Docker/Kubernetes), GitHub Actions, and automation.
* Practical experience implementing LLM and GenAI solutions, including prompt engineering, model fine‑tuning, RAG pipelines, embeddings, and vector databases.
* Build scalable data pipelines and workflows on GCP (BigQuery, Vertex AI, Dataflow, Pub/Sub, Redis, NoSQL).
* Optimise model performance, monitor production systems, and ensure reliability with auto‑scaling using Prometheus, Dynatrace, and LangSmith.
Desirable skills/knowledge/experience
* Strong experience building and deploying machine learning models, including preprocessing, feature engineering, training, evaluation, and optimisation.
* Knowledge of API gateways and Istio, ability to diagnose and intercept failures in end‑to‑end communication.
* Implementation of data governance, security, and MLOps best practices on GCP.
* Proficiency with Python and AI/ML frameworks such as TensorFlow, PyTorch, JAX, scikit‑learn, and Hugging Face libraries.
* Expertise in MLOps and LLMOps practices—including CI/CD, model registry/versioning, feature stores, orchestration, and automated deployments.
* Ensuring AI solutions meet security, privacy, compliance, and responsible AI standards.
* Understanding of secure engineering and data protection practices (IAM, secrets management, encryption, safe handling of sensitive data).
* Ability to optimise training and inference pipelines (profiling, quantisation, distillation, batching, caching, or hardware acceleration).
* Collaboration with data scientists to productionise models and integrate them into applications, workflows, and APIs.
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