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Mishcon de Reya LLP seeks an Automation & AI Engineer to translate the AI and innovation roadmap into practical, secure services. You will lead senior technical work across legal practice and business operations, building AI-enabled workflows and integrations with a focus on reliability and governance.
You will collaborate with Product, Data, Security, and Architecture teams, set engineering standards, review designs, and mentor peers while balancing performance and cost in complex environments.
The AI and Engineering team develops practical AI, automation and integration capability for legal practice and business operations. It works closely with Product, Data, Architecture, Security and operational teams to turn valuable ideas into secure, maintainable services, while retaining space for controlled experimentation and rapid learning. Reporting to the AI & Innovation Lead, the Automation & AI Engineer is the senior technical specialist responsible for turning the AI and innovation roadmap into working capability.
Lead the technical design and build of complex AI-enabled and automated workflows across legal practice and business operations. Work against the prioritised AI and innovation roadmap, with the Product Manager accountable for product priorities and outcomes and the AI & Innovation Lead accountable for innovation direction and team capability. Translate product priorities into achievable technical approaches, prototypes and delivery plans, making build, buy and integration trade-offs clear. Set practical standards for Python and TypeScript development, APIs, testing, source control, CI/CD, observability and support readiness. Review designs and code, coach engineering colleagues and create reusable patterns rather than one-off solutions. Design and build agents and MCP clients and servers that connect approved models to firm data, tools and workflows. Define secure tool boundaries, authentication, authorisation, audit trails, and human approval points for agentic actions. Set evaluation and release standards for AI-enabled services, reviewing evidence on task completion, quality, safety, latency, reliability and cost before production use. Manage token usage and model choice pragmatically, balancing performance, context, reliability and consumption cost. Use AI-assisted software development to create prototypes quickly, while applying engineering review and production controls before wider use. Work with the Product Manager and AI & Innovation Lead to shape experiments, PoCs and MVPs with clear hypotheses, measures and stop, iterate or scale decisions. Productionise successful experiments through appropriate architecture, testing, monitoring, runbooks, release controls and ownership. Partner with Security, Risk, Data and Architecture to protect confidential information and meet firm standards. Build fallback paths, failure handling and operational monitoring into AI and automation services. Support live services, investigate failures and turn incidents and evaluation findings into engineering improvements.
Strong software engineering fundamentals, including version control, testing, code review, documentation and secure development. Experience working with APIs, integrations, cloud services and event-driven or workflow-based solutions. Ability to move from an unclear problem to a working prototype, then improve it using evidence and user feedback. Confidence working with Product, Data, Architecture, Security and business specialists in a multidisciplinary environment. Clear written and verbal communication, including the ability to explain technical trade-offs to non-technical stakeholders. A pragmatic approach to experimentation, with good judgement about what is safe to test and what is ready to operate. Use of approved identity, secrets-management and service-connection patterns, avoiding embedded credentials and unmanaged service accounts. Commitment to accessible and usable user-facing applications and workflow interfaces. Substantial hands‑on experience building and operating AI-enabled applications, automation or integration services. Strong Python and TypeScript capability, with experience designing production APIs and cloud‑native integrations. Practical experience with LLM applications, tool use, agents, RAG, evaluation and MCP or comparable integration patterns. Evidence of technical leadership through design decisions, peer review, reusable standards and coaching.
We are proud of our agile working culture and are always happy to talk flexible working.