• Design and own end-to-end AI solution architectures, covering data pipelines, AI services, integrations, and infrastructure requirements. • Define camera setups, imaging requirements, and hardware specifications for computer vision use cases. • Coordinate installations and integrations with internal teams. • Collaborate with Digital, IT Infrastructure, Software Engineering, and Master Data teams to ensure scalable and maintainable solutions. • Develop and deploy AI models on edge devices where low latency or offline processing is required. • Create and optimize conversational AI for natural language interactions and improved user experiences. • Continuously explore and incorporate emerging AI/ML technologies to enhance the efficiency and scalability of solutions. • Monitor model performance and conduct regular evaluations to maintain accuracy and reliability. • Contribute to bi-weekly brainstorming and team innovation sessions to propose new ideas and solutions.

Role Description We are looking for a talented Senior AI Engineer to join a brand new delivery squad focused on solving new client problems in 4-week cycles. Each project is designed to produce a working pilot that demonstrates clear value, not a polished long-term system. You’ll work in a 4-person team (AI Engineer, Product Manager, Data Scientist, Principal for AI Engineering), using AI coding tools (e.g. Claude Code) to reduce build time and get solutions into a usable state quickly. The role includes direct interaction with clients, where you’ll help shape solutions and explain technical decisions. - Experiment with POCs to find solutions for real-world problems using Large Language Models. - Collaborate on AI-driven projects, working alongside engineers, product managers and Data Scientists while maintaining clear documentation. - Build Agentic solutions with LangGraph/ADK. - Familiar with different multi agent system patterns. - Build and deploy LLM-based solutions using RAG. - Familiar with different types of databases: Relational, Vector, Graph etc. - Design and optimise APIs using Python and FastAPI to serve AI solutions. - Familiar with GCP ecosystem and Cloudrun. - Build and optimise data pipelines for vector search and knowledge retrieval using Vector databases and embedding models. Qualifications - Experience building production LLM systems used by clients/customers. - Background in Software Engineering with Python. - Solid understanding of the Python standard library and modern Python coding, testing, debugging and automation techniques. - Hands-on experience building solutions using LLMs and Agentic architectures with ADK, LlamaIndex, or LangGraph. - Experience using AI-Assisted coding tools (such as Claude Code) to help accelerate development cycles. - Ability to deliver usable outputs within 4 weeks. - Working with vector databases for embedding and indexing. - Strong experience with cloud platforms. - Strong experience with API design and frameworks like FastAPI or Flask. - Solid experience with relational databases and SQL. - Interest in expanding your knowledge into GenAI and machine learning. - Excellent communication skills and the ability to work well in a collaborative team environment. Requirements - Nice to have: Strong experience with GCP. - Experience with Graph databases. - Experience in bringing data-intensive projects into production. - Experience with CI/CD pipelines. Benefits - Remote Contract (UK Candidates only). - Ideal start date: ASAP. - Contract Duration: 6 Months. - Outside IR35. - Rate: £500-600 p/d. - Work Environment: Fully Remote in the UK. Company Description Elsewhen, a London-based consultancy, designs and builds technology solutions for clients like Spotify, Google, Inmarsat, and Zego. Over the past decade, we have built a workplace prioritising impact, drive, and friendliness. We value outcomes over hours and agility over rigid processes. Our Commitment to Diversity: Diverse thoughts, backgrounds, and perspectives create stronger teams and better technology. We welcome everyone, regardless of culture, appearance, or perspective, fostering individuality. We empower our team to challenge norms, grow ideas, and produce their best work.

• Build and maintain end-to-end deployment pipelines for AI-powered applications, including artifact builds, environment promotion, rollback, and observability hooks. • Stand up and operate the runtime and lifecycle infrastructure for production agents, including deployment, versioning, monitoring, rate-limiting, and retirement. • Own how the organization provisions, rotates, scopes, and meters access to model provider APIs (Anthropic, OpenAI, and others). • Build evals into the CI/CD pipeline so no agent or LLM-powered service ships without passing a defined eval bar. • Stand up self-hosted inference for workloads where managed APIs aren't the right fit. • Design and build the shared developer harness that every AI-powered service uses. • Partner with finance on cost visibility, including token accounting and per-feature cost attribution. • Write documentation, runbooks, and clear interfaces. • Participate in code review and promote collaboration and best practices.

• Drive the architecture, development and optimization of (generative) AI applications with a strong focus on model evaluation, retrieval-augmented generation (RAG), and prompt engineering. • Implement end-to-end RAG pipelines (ingestion, chunking, embeddings, retrieval, re-ranking) and integrate them with internal data sources. • Design and integrate data storage for AI applications using vector search in Snowflake and relational databases such as PostgreSQL. • Analyze and integrate data from various sources, ensuring high data quality and preparing it for effective AIdriven solutions. • Experiment with LLM models (e.g. OpenAI, Anthropic, Azure OpenAI) to improve performance, reliability and applicability across business use cases. • Establish and maintain robust engineering practices for GenAI applications (testing, observability, security, performance optimization). • Collaborate closely with our AI platform team to enhance, standardize and scale AI-driven capabilities across the BMW Group. • Work closely with different business units, helping them leverage AI models for real-world applications and continuous improvements. • Share your expertise in Generative AI, LLM orchestration, and software engineering with other teams, contributing to knowledge-sharing and innovation within the organization.