Ai Backend Engineer Job at ITV Jobs (White City, London / Media City, Manchester)

Ai Backend Engineer

ITV Jobs

Location:
United Kingdom , White City, London / Media City, Manchester

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Not provided

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Job Description:

We are seeking a Backend Engineer to join our Group Data team, working as part of the AI Agent Hub delivery team. This role will help build, extend, and improve the backend services, APIs, integrations, and AI platform capabilities that support ITV’s emerging internal AI platform. The role has a primary focus on Python, FastAPI, Open WebUI backend extensions, LiteLLM integration, RAG pipelines, knowledge ingestion, model routing, secure internal system integration, evaluation workflows, and operationally robust AI services. You will work closely with frontend, platform, product, design, architecture, cyber security, and data colleagues to create secure, reliable, and maintainable backend capabilities that allow ITV teams to use foundational models, assistants, agents, multimodal tools, and internal system integrations through a single governed experience. The role sits initially in a small, hands-on, stream-aligned delivery team focused on shipping a high-value internal product for ITV Studios and the wider business. It is well suited to someone who enjoys building practical backend services close to product outcomes, working across API and platform boundaries, and translating emerging AI platform capabilities into dependable services that are easy for others to use.

Job Responsibility:

Build and improve backend services for the AI Agent Hub, primarily using Python, FastAPI, Open WebUI backend patterns, and OpenAI-compatible APIs
Develop APIs and integration services that support chat, assistant configuration, agent workflows, knowledge management, permissions, admin features, and usage visibility
Integrate backend services with LiteLLM or similar gateway capabilities for model routing, model aliases, virtual keys, user attribution, rate limits, budgets, retries, fallback behaviour, and usage reporting
Build and maintain RAG capabilities, including document ingestion, data cleaning, chunking, embeddings, retrieval, citation support, and vector store integration
Help improve the quality and reliability of knowledge pipelines by validating source data, detecting poor-quality inputs, and supporting evaluation-first approaches to retrieval and response quality
Build secure integration patterns for internal systems, APIs, knowledge sources, storage platforms, workflow automation tools, and MCP-based capabilities
Implement backend controls that support responsible AI use, including access control, audit logging, provenance capture, prompt and output guardrail integration (e.g. personal data masking), and human-in-the-loop review patterns
Work closely with frontend and platform engineers to align backend APIs with user experience needs, authentication flows, observability, deployment patterns, and operational support requirements
Run and debug the full application stack locally using Docker and Docker Compose, including reading logs, editing configuration, understanding image builds, and diagnosing environment issues
Contribute to automated testing, technical design, pull requests, runbooks, support documentation, and engineering standards
Over time, support wider AI platform adoption across ITV by helping teams connect data, expose tools safely, configure assistants and agents, and embed AI capabilities into existing workflows

Requirements:

Experience building and supporting production backend services, ideally with 3+ years of applied backend engineering experience
Strong Python skills, including API development, testing, debugging, dependency management, and maintainable service design
Experience building REST APIs with FastAPI, Flask, Django, Node.js, or similar backend frameworks, with a willingness to work primarily in Python and FastAPI
Experience integrating backend services with third-party APIs, internal APIs, authentication providers, storage systems, and asynchronous or long-running workflows
Practical understanding of LLM-powered product experiences, including prompt and system context, model selection, context windows, token usage, streaming responses, error handling, and fallback states
Experience integrating with LLM APIs or OpenAI-compatible endpoints, or a strong interest in learning how these patterns work in a governed enterprise platform
Familiarity with RAG concepts such as document ingestion, chunking, embeddings, vector stores, retrieval quality, citations, and grounding responses in source material
Comfortable working with databases and storage technologies such as PostgreSQL, SQLite, Redis, object storage, or vector stores
Comfortable working with Git, pull requests, code review, automated testing, dependency updates, and local development using Docker or Docker Compose
Good understanding of security and privacy fundamentals for backend services, including authentication, authorisation, role-based access control, secrets management, audit logging, and safe handling of user data
Strong communication skills and the ability to explain trade-offs clearly, collaborate constructively, and contribute to technical discussions across product, design, engineering, architecture, cyber security, and data

Nice to have:

Experience with Open WebUI backend customisation, Functions, Tools, Pipelines, or similar extension patterns in open-source AI platforms
Experience with LiteLLM or similar LLM gateways, including model routing, aliases, virtual keys, budgets, cost tracking, retries, fallback chains, and provider abstraction
Experience with asynchronous Python, background workers, job queues, event-driven processing, WebSockets, SSE, or streaming API patterns
Experience with data ingestion from unstructured sources such as PDFs, scripts, documents, transcripts, metadata exports, spreadsheets, or enterprise knowledge bases
Experience with vector stores or search technologies such as Postgres pgvector, Chroma, Qdrant, Pinecone, Weaviate, Amazon S3 Vector, OpenSearch, or hybrid keyword and vector retrieval
Familiarity with RAG evaluation approaches and tools such as RAGAS, DeepEval, LangSmith, or custom evaluation datasets and metrics
Experience with SSO and enterprise access patterns, ideally OIDC, OAuth2, SAML, Okta, Amazon Cognito, JWTs, or RBAC for internal tools
Experience with Kubernetes, Helm, GitHub Actions, CI/CD pipelines, environment management, or production deployment workflows
Familiarity with observability concepts, ideally including OpenTelemetry, Prometheus, Grafana, structured logs, traces, usage dashboards, and cost dashboards
Familiarity with AI security risks such as prompt injection, data leakage, unsafe tool use, over-permissive retrieval, and the need for guardrails and auditability

What we offer:

Flexible working with a range of options
Generous holiday allowance, plus you can buy more
Annual bonus opportunity
Competitive pension contribution
Save as you earn - with an opportunity to buy ITV shares
Wellbeing and volunteering days plus a wide range of opportunities to help you live a balanced and healthy life

Additional Information:

Job Posted:
May 16, 2026

Expiration:
May 25, 2026

Employment Type:

Fulltime

Work Type:

Hybrid work

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