At a Glance
- Tasks: Lead AI solution delivery and build innovative workflows in a hybrid work environment.
- Company: Dynamic tech firm focused on AI and automation solutions.
- Benefits: Competitive pay, flexible working, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on innovation and career advancement.
- Why this job: Join a cutting-edge team and tackle real-world challenges with AI technology.
- Qualifications: Experience in Azure, AI development, and strong problem-solving skills required.
The predicted salary is between 60000 - 80000 £ per year.
Role type: Contract (Inside IR35)
Location: London
Work mode: Hybrid – at least 1 or 2 days a week onsite
Key Responsibilities
- Delivery & Execution
- Lead end-to-end solution delivery across Corporate, Compliance, Legal, and Risk Management domains — from design and build through UAT and production deployment.
- Own release readiness: manage UAT-to-production transitions, validate operational quality, and ensure solutions meet the firm's engineering and compliance standards.
- Build and maintain CI/CD pipelines and DevOps processes on Azure-based platform, working within established GitOps and Flux deployment patterns.
- Manage delivery across multiple concurrent workstreams, coordinating with engineering, product, and business teams to maintain momentum and remove blockers.
- AI-Enabled Solution Design & Build
- Apply AI tools, agent frameworks, and workflow orchestration to solve business-critical problems — selecting the right approach and model for each use case rather than defaulting to the most complex solution.
- Design and implement agent workflows on Axon SDK and Azure AI Foundry, integrating Azure OpenAI, Document Intelligence, and AI Search.
- Build multi-step automations and AI pipelines using Temporal as the firm's enterprise workflow orchestrator.
- Integrate solutions with data platform — consuming Snowflake data through the Delphi data PaaS, connecting to operational systems, and working within the firm's data governance model.
- Expose and consume capabilities through internal Model Context Protocol (MCP) broker pattern.
- Stakeholder Engagement
- Work directly with business leads, product managers, and domain teams to gather requirements, shape use cases, and translate them into clear, deliverable technical solutions.
- Communicate progress, risks, and trade-offs clearly to both technical leads and senior business stakeholders.
- Contribute to solution documentation, runbooks, and knowledge transfer as a standard part of delivery.
Required Skills & Experience
- Engineering & Delivery
- Proven track record of end-to-end solution delivery in production enterprise environments — design through deployment and ongoing operation.
- Strong hands-on experience with Azure: AKS, Azure AI services, CI/CD pipeline implementation, and DevOps practices.
- Solid understanding of GitOps deployment patterns and Flux-based release workflows.
- Experience writing infrastructure configuration — Terraform or equivalent IaC — in a team-managed environment.
- Comfortable working across the full delivery lifecycle including UAT coordination, operational readiness, and post-launch support.
- AI & Automation
- Deep practical experience with AI development workflows: agent design, prompt engineering, workflow orchestration, and model selection.
- Hands-on experience with AI frameworks and orchestration tools — applied to real delivery problems, not proof-of-concept work.
- Fluency with AI-assisted development tooling (Cursor, Claude, Codex) as a primary accelerant for engineering delivery.
- Working knowledge of Azure AI Foundry, Azure OpenAI, Document Intelligence, and AI Search is strongly preferred.
- Experience with Temporal or a comparable workflow orchestration platform is a plus.
- Domain & Environment
- Exposure to Corporate, Compliance, Legal, or Risk Management functions; understanding of the domain accelerates delivery significantly.
- Strong communication and stakeholder management skills; ability to operate credibly at both working and senior levels.
- Ownership mindset, high accountability, and the flexibility to meet delivery commitments in a demanding environment.
- AI & Agents
- Axon SDK · Azure AI Foundry · Azure OpenAI · Document Intelligence · AI Search · MCP broker.
- Orchestration
- Temporal — agent runs, ETL pipelines, multi-step automations.
- Data
- Snowflake via Delphi · Event Hubs · ADLS Gen2 · Cosmos DB · Redis · EDB Postgres.
- Cloud & Deployment
- Azure · AKS · Flux GitOps · GitHub Actions · Terraform central module catalog.
- Observability
- Datadog · Prometheus / Grafana · PagerDuty.
- ITSM & Identity
- ServiceNow · Jira · Okta (OIDC) · Active Directory.
Forward Deployed AI Engineer in Slough employer: ixceed solutions
As a Forward Deployed AI Engineer at our London office, you will thrive in a dynamic hybrid work environment that fosters innovation and collaboration. We prioritise employee growth through continuous learning opportunities and a supportive culture that values your contributions to impactful projects across Corporate, Compliance, Legal, and Risk Management domains. Join us to be part of a forward-thinking team that embraces cutting-edge technology while enjoying the unique advantages of working in one of the world's most vibrant cities.
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We think you need these skills to ace Forward Deployed AI Engineer in Slough
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
