Department overview:

The Front Office AI Technology Team sits within the Front Office Technology department and provides a shared capability for the development, operation, and adoption of AI across the firm. The team is responsible for building and supporting enterprise‑grade AI capabilities, including LLM‑powered applications, retrieval‑augmented generation (RAG) systems, agent‑assisted workflows, and scalable internal AI tooling.

We develop the core AI foundations required to deploy AI safely and at scale, while also working closely with the business to ensure these capabilities are used effectively in day‑to‑day workflows. This includes supporting experimentation, guiding practical adoption, and helping teams embed AI into real processes where it delivers measurable benefit.

A key focus of The Front Office AI Technology Team is ensuring that AI solutions are reliable, secure, and aligned with the firm’s control and risk frameworks. The team balances innovation with discipline, providing common tooling, patterns, and guidance that allow AI to be used consistently and responsibly across research and operational contexts.

Role overview:

Key Responsibilities:

AI Enablement & Adoption

• Act as a go‑to point of contact for teams who want to understand what AI tools are available and how to use them effectively.
• Run workshops, demos, and hands‑on sessions that help users understand the benefit of using LLMs and emerging AI related technologies both for business users and more technical teams.
• Support day‑to‑day adoption of Microsoft Copilot, ChatGPT and Claude alongside other approved AI tools by embedding them into real workflows.

Training & Best Practice

• Deliver practical training on prompt engineering, AI limitations, and good usage patterns for both technical and non‑technical audiences.
• Create and maintain reusable materials such as prompt examples/libraries, walkthroughs, and short guidance notes.
• Continuously refine training content based on user feedback and emerging best practice.

Understanding Business Processes

• Spend time with teams to understand their existing processes, pain points, and where work is slow, repetitive, or manual.
• Help teams articulate problems clearly enough that AI tooling can be applied sensibly.
• Map simple end‑to‑end workflows and identify realistic opportunities for AI assistance.

Light Prototyping & Applied AI

• Build simple prototypes or proof‑of‑concept workflows using Python, internal libraries, or approved AI APIs alongside tools such as MS Copilot Studio.
• Pair with engineers or platform teams when ideas move beyond quick prototypes.
• Focus on small, shippable improvements rather than large, speculative solutions.

Feedback & Continuous Improvement

• Collect structured feedback on what works, what doesn’t, and where users get stuck.
• Share insights with The Front Office AI Tech Team, governance, and Infrastructure Tech teams to help guide tooling, documentation, and prioritisation.
• Help surface recurring themes rather than one‑off requests.

Adoption Metrics, Reporting & Insight

• Support the definition and tracking of adoption metrics for approved internal AI tools.
• Work with Front Office Technology and platform teams to help maintain simple reporting and dashboards that show usage and engagement patterns (for example: active users, frequency of use, and common use cases).
• Monitor adoption trends and identify areas of low engagement or friction that may require additional enablement, training, or tooling changes.
• Combine quantitative usage data with qualitative user feedback to build a clear view of how AI tools are being used in practice.
• Share regular adoption insights with relevant stakeholders to inform prioritisation of training materials, documentation, and incremental improvements to AI tooling.

Why this role exists:

The firm is investing heavily in modern AI platforms and tools, but technology alone does not create value. Real impact comes when teams change how they work. This role exists to help front‑office and technology teams translate AI capability into everyday practice, replacing one‑off experimentation with well‑understood patterns, better habits, and repeatable workflows. Through hands‑on enablement, training, and applied problem‑solving, the AI Adoption SME ensures that AI use is both effective and appropriately governed, allowing teams to move faster without increasing operational or compliance risk.

What makes this role different:

This role offers the chance to work directly with trading desks, providing a rare and exciting opportunity to gain direct front-office exposure. You’ll work at the intersection of business context and applied AI: understanding how teams operate, then helping them adopt approved AI tools safely and effectively through training, workflow mapping, and lightweight prototyping. It’s ideal for someone who enjoys ambiguity, values practical impact over hype, and wants to see their work change how teams operate day-to-day.

Skills & Experience

Essential:

• Demonstrated interest in evolving applied AI technologies and their use in improving real business processes in a practical and controlled manner.
• Experience delivering or supporting training, workshops, or enablement sessions for technical and non‑technical audiences.
• Hands‑on experience using modern AI tools beyond typical end‑user interaction, including:
                  - Practical understanding of prompt engineering techniques and common LLM failure modes
                  - Experience grounding AI outputs in data (e.g. through document retrieval, APIs, MCP or structured context rather than free‑text prompting alone)
                  - Exposure to designing AI‑assisted workflows that support repeatable tasks rather than one‑off interactions.
• Familiarity with enterprise and low‑code AI tooling, such as Microsoft Power Apps, Power Automate, Copilot Studio, Claude Cowork, or similar workflow and AI enablement platforms.
• Practical experience assessing AI output quality and limitations for real business use cases, and iterating prompts or workflows to improve reliability.
• Comfortable working with Python at a practical level (scripts, APIs, data handling), without requiring production‑grade engineering expertise.
• Strong communication skills, with the ability to explain technical concepts clearly to non‑technical users.
• Comfortable operating in environments with evolving requirements, and able to iterate pragmatically rather than waiting for complete specifications.

Desirable:

• Familiarity with retrieval‑augmented generation (RAG) concepts at a practical level, such as working with internal documents or structured reference data.
• Experience designing or supporting simple AI‑assisted workflows for document handling, summarisation, data extraction, or knowledge access.
• Awareness of responsible AI considerations in regulated environments, including basic data handling, validation of outputs, and appropriate human review.
• Exposure to lightweight agent‑style patterns (e.g. tool use, structured outputs, task decomposition), without requiring ownership of complex agent frameworks.

About you:

You are a bright and approachable individual who works well with others and enjoys understanding how different teams operate in practice. You are comfortable engaging with both technical and non‑technical stakeholders and are able to communicate clearly and pragmatically. Alongside your technical experience, you may bring experience from a client‑facing or commercially oriented role earlier in your career, which helps you understand business priorities and frame solutions in a way that resonates with users.

You have a strong interest in applied AI and a genuine curiosity about how emerging tools can improve the way people work. You are motivated to explore new capabilities, experiment hands‑on, and understand where AI can add value in real workflows. Your approach is practical rather than theoretical, and you are focused on achievable improvements rather than technology for its own sake.

You take a considered and responsible approach to AI adoption. You are aware of the limitations of AI tools and the importance of appropriate controls, validation, and oversight, particularly in a regulated environment. You are comfortable discussing both the benefits and constraints of AI with users, helping set realistic expectations and encouraging sensible use.

You work well in collaborative environments where priorities and requirements evolve. You are adaptable, open to feedback, and comfortable learning by doing. You take satisfaction from helping others build confidence with new tools and from supporting adoption across teams, bringing a constructive and measured mindset to your work.

BlueCrest is committed to providing an inclusive environment for its workforce. As an employer, we provide equal opportunities to all people regardless of their gender, marital or civil partnership status, race, religion or ethnicity, disability, age, sexual orientation or nationality.

Department overview:

This role sits at the intersection of our Front Office AI Technology team and Technology Infrastructure. This role straddles both teams, operating as the delivery and implementation function for our central AI capacity within the broader Infrastructure department. The position is responsible for deploying and operationalising the Front Office AI Technology team’s requirements by working hands‑on with Technology Infrastructure to design, build, automate, and run AI‑ready cloud platforms in production.

The Front Office AI Technology team is focused on enabling the responsible for development of AI capabilities within the investment environment. The team partners closely with Front Office stakeholders to identify, design, and develop AI-enabled solutions that enhance decision‑making, efficiency, and insight, while operating within the firm’s risk, control, and regulatory constraints.

Our Technology Infrastructure team operates globally and is responsible for every aspect of the firm's hybrid platform. This ranges from our EUC/Office environments to Trading and Core service Co-Location Data Centres, and extends to Public Cloud, delivering top-tier technology services to a dynamic and demanding Trading organisation.

In addition to meeting the round-the-clock operational demands of the platforms, we continuously evolve and transform our platforms to maintain a competitive edge that our business requires. We innovate to provide valuable solutions and leverage our skilled Technology teams to deliver against rapidly changing business requirements.

Role overview:

The AI and Public Cloud Engineer is a highly technical, hands‑on platform engineering role responsible for designing, building, automating and operating secure cloud and AI platform foundations across a hybrid environment.

The role exists to accelerate public cloud based AI maturity while enabling enterprise and Front Office AI use cases through robust, well‑governed infrastructure.

Working primarily across Microsoft Azure and later with AWS and GCP Gemini / VertexAI enablement, the role delivers secure by design platforms covering identity, networking, security, governance, automation, and cost control. The position focuses on the operational reality of AI services with supporting, integrating, and governing AI platforms and AI SaaS providers within a low risk tolerance, regulated environment.

This role offers a compelling opportunity to operate at the intersection of AI innovation and institutional‑grade infrastructure, enabling next‑generation AI capabilities within the investment environment of a leading hedge fund.

Experience required:

Public Cloud Platform Engineering (Azure/AWS/GCP)

• Deep hands-on experience engineering and operating Azure and/or AWS landing zones, including identity, networking, security baselines, governance and hybrid connectivity.
• Working knowledge of GCP infrastructure sufficient to deploy, integrate, and operate Vertex AI / Gemini alongside existing cloud platforms.

AI Platform Operations & Integration

• Proven experience building, running, and supporting AI services across public cloud (Azure, AWS, GCP, etc.) and enterprise SaaS AI platforms (OpenAI Enterprise, Anthropic, etc.).
• Strong understanding of AI operational patterns including API-based model access, availability, monitoring, and governance rather than model development or data science.

Cloud Networking & Hybrid Connectivity

• Hands-on experience designing and operating hybrid cloud connectivity (ExpressRoute / Direct Connect), including resilient routing, segmentation and private access patterns.
• Strong practical knowledge of cloud network security, private endpoints, DNS integration, egress control and zero-trust networking principles.

Identity, Access Management & Security

• Deep experience implementing cloud IAM (Azure Entra ID, AWS IAM), RBAC, privileged access, MFA and workload identity patterns.
• Practical application of security-by-design principles across cloud and AI platforms, including least privilege, auditability and controlled access to sensitive data.

Infrastructure as Code & Automation

• High proficiency in Infrastructure as Code (Terraform, Bicep, CloudFormation/CDK) to deliver repeatable, governed cloud platforms.
• Strong scripting or development capability (Python, Bash, PowerShell) to automate platform operations, integrations, and CI/CD workflows.

Governance, Compliance & Cost Management

• Experience implementing cloud governance frameworks including policy enforcement, tagging standards, compliance automation and environment guardrails.
• Practical experience managing cloud cost controls and consumption optimisation, particularly for AI and data-intensive workloads.
• Hands-on experience applying data classification, DLP, and AI usage controls to protect sensitive data across cloud and SaaS AI services.
• Working knowledge of enterprise AI security and governance standards aligned to regulated or low risk tolerance environments.

Platform Support & Operational Excellence

• Demonstrated experience supporting production cloud platforms, including incident response, resilience testing and operational improvement.
• Ability to balance critical BAU support with ongoing platform engineering and transformation work in time‑sensitive environments.

About you:

The ideal candidate is a highly technical, hands-on platform engineer with demonstrable broad-range skills and deep experience in public cloud infrastructure and AI service delivery. You have a proven track record in problem-solving, balancing critical operational support with advanced engineering initiatives in fast-paced, demanding environments.

You are passionate about emerging cloud and AI technologies, eager to build innovative solutions that enhance technology performance while naturally understanding the need for rapid delivery balanced against business risk and operational constraints. You appreciate the importance of security, governance, and cost discipline in enterprise cloud deployments, especially when enabling AI capabilities that require strong data protection and regulatory alignment.

You have excellent communication and organizational skills, working effectively both independently and as part of cross-functional teams across Technology Infrastructure, Cybersecurity, Application Development, and Front Office stakeholders. Your commitment to continuous improvement is evident in both learning from others and sharing knowledge to enhance team capability and platform maturity

BlueCrest is committed to providing an inclusive environment for its workforce. As an employer, we provide equal opportunities to all people regardless of their gender, marital or civil partnership status, race, religion or ethnicity, disability, age, sexual orientation or nationality.