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# Senior AI Platform Engineer

- **Company:** Emporia Consulting Group Limited
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £286,000.0
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, C Sharp (Programming Language), Cloud Engineering, Continuous Integration, Data Structures, Data Stores, Distributed Systems, Internet Hosting Service, Python (Programming Language), Machine Learning, NoSQL, Search Technologies, Secure Coding, Software Engineering, SQL Databases, TypeScript, AI Infrastructure, .NET Core, Large Language Models, Generative AI, AI Platforms, Machine Learning Operations, Serverless Computing, Microservices
- **Published:** September 16, 2026
- **Apply:** https://www.totaljobs.com/job/senior-ai-engineer/emporia-consulting-group-limited-job107986086

## About the Role

* Experience building, deploying, and maintaining machine learning and generative AI solutions (e.g., LLMs, embeddings, retrieval patterns, vector search, model fine-tuning).
 * Strong MLOps knowledge across continuous integration, deployment, monitoring, and evaluation of AI/ML models.
 * Experience with AI infrastructure (orchestration pipelines, GPU compute, model hosting services) and scalable AI data structures (including vector databases/semantic search).
 * Deep software engineering capability in .NET Core/C# and modern back-end engineering, with flexibility to work across languages such as Python, Go, or TypeScript.
 * Strong understanding of distributed systems, microservices, cloud-native patterns, and modern data stores (SQL and NoSQL).
 * Secure coding mindset with awareness of cybersecurity risks, particularly around AI and data privacy.
 * Proven technical leadership (principal engineer/tech lead/architect level), with a track record of mentoring and influencing engineering direction.

## Description

* Leading the design and delivery of AI-enabled enterprise platforms, from early architecture through to production.
 * Building and improving applied AI solutions (including LLM-based patterns such as embeddings, retrieval approaches, and model tuning where appropriate).
 * Setting engineering standards for MLOps: CI/CD, model deployment, monitoring, evaluation, and operational reliability.
 * Architecting secure, cloud-native services (microservices, APIs, messaging/event-driven patterns) that support AI workloads at scale.
 * Coaching engineers and collaborating across Engineering, Data Science, and Product to turn complex AI concepts into practical delivery plans.


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