AI Engineer

Description

My Financial Services client is seeking to recruit a AI Engineer on an initial 6 month contract based in London. It is hybrid and will require 3x days onsite per week.

As a Back-End AI Engineer, you will design and deploy secure, scalable AI services that power next-generation use cases across client intelligence, document processing, and risk management. You'll work in a greenfield environment, building compliant AI pipelines using Gemini (GCP), Azure OpenAI or Self Hosting embedding security and privacy controls from experimentation to production, in alignment with the bank's cybersecurity and regulatory standards.

Accountabilities & Responsibilities

Architect and implement secure AI services from lab to production, ensuring scalability and compliance

Develop robust APIs for LLMs, RAG pipelines, agentic workflows and document intelligence systems

Embed cybersecurity and data privacy controls across all AI workflows (e.g., encryption, anonymisation, access logging)

Collaborate with the CISO function on threat modeling, security reviews, and AI-specific control design.

Integrate with enterprise IAM systems, enforcing RBAC, least privilege

Conduct vulnerability scans, pen-test remediation, and support internal and regulatory audits (FCA, PRA)

Required Knowledge & Experience

Delivered greenfield AI systems in production with secure-by-design architecture

Designed and managed AI lab environments using IaC, containerisation, and secure networking practices

Hands-on experience with LLM implementation, including fine-tuning, prompt engineering, and secure deployment

Built agentic workflows using modular LLM agents with memory, planning, and tool integration

Implemented Model Context Protocol (MCP) to manage secure, auditable context injection across agentic systems

Experience building RAG pipelines with strict data governance and contextual integrity

Familiarity with EU AI Act, FCA cybersecurity principles, and oversight of critical systems

Worked directly with cybersecurity and compliance teams in regulated deployments

Implemented or maintained controls under ISO 27001, NIST, or SOC2 frameworks

Technical Skills & Technologies:

Languages & Frameworks

Python (FastAPI), LangChain, Google AI SDK, Azure Open AI SDK

Cloud & AI Platforms

GCP: Vertex AI, Gemini API, Cloud Run, GCS, IAM, Secret Manager, Audit Logs

Azure: Azure ML, Azure OpenAI, Key Vault, Azure Policy

Experience with Self Hosting

LLM

Fine-tuning and prompt engineering for LLMs (e.g., GPT, Gemini, Claude)

Secure deployment of LLMs via APIs with input/output filtering and logging

Integration of LLMs into RAG pipelines, document intelligence, and agentic workflows

Use of vector databases (e.g., FAISS, Pinecone, Chroma) for semantic search and retrieval

Implementation of grounding, context injection, and response validation mechanisms

Model Context Protocol (MCP)

Implement secure, policy-aligned Model Context Protocol (MCP) for managing contextual memory, grounding, and session control in LLM-based systems

Enforce context boundary policies, context versioning, and traceability to support auditability and prevent data leakage

Integrate MCP with enterprise IAM and data governance frameworks to ensure compliant context injection and revocation

Agentic Workflows

Design and orchestrate agentic AI workflows using modular, goal-driven agents with memory, planning, and tool-use capabilities

Implement secure agent execution environments with task decomposition, tool chaining, and feedback loops

Integrate agents with enterprise systems (e.g., document stores, APIs, risk engines) while enforcing contextual integrity, rate limiting, and audit logging

Apply agentic patterns to automate complex financial tasks such as client onboarding, document summarisation, and risk signal extraction

Security Tooling

Static code analysis (Bandit, SonarQube)

Secrets scanning, encryption (at rest/in-transit), token management

Identity integration (Google Identity, Azure Entra ID)

Data Security & Governance

RAG pipelines with data classification, masking, and DLP

GDPR and data residency compliance

MLOps & DevSecOps

GitHub Actions, CI/CD security testing, model drift detection, audit logging

Lab Environment Tooling

Infrastructure-as-Code (IaC): Terraform, Pulumi

Containerization & Orchestration: Docker, Kubernetes (GKE/AKS)

Networking & Isolation: VPCs, private endpoints, firewall rules, network policies

Data Sandboxing: Synthetic datasets, masking, DLP tooling

Monitoring & Observability: Prometheus, Grafana, Cloud Logging

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