Role Description

• Responsible for building production-ready backend services for agentic workflow
  components aligned to solution architecture and platform standards.
• Implement solution designs by building Python services, worker processes, and
  reusable libraries following defined architecture, patterns, and standards.
• Develop agentic workflow components: tool connectors, orchestration steps, state
  management modules, retrieval components, and approval/escalation flows.
• Build reliable LLM interaction layers: tool/function calling, schema-validated
  structured outputs, guardrails, safe tool execution boundaries, and fallback
  behaviors.
• Implement robust backend patterns: async execution, job queues,
  retries/idempotency, compensating actions, and failure isolation for long-running
  workflows.
• Deliver production readiness: logging/tracing, metrics, decision logs, run replay
  support, performance profiling, and cost/latency controls.
• Write clean, maintainable, testable code with strong review discipline:
  unit/integration tests and regression testing for prompts/agents where applicable.
• Collaborate closely with the Agentic AI Architect and technical leads; support
  delivery across DEV/UAT/PROD including defect triage and operational support.

Location

• The role supports one of our top-tier banking clients in London (Canary Wharf) and
  requires a minimum of three days on-site presence.
• This is a permanent position based in the UK. We will only consider applicants who
  are eligible to work in the UK. For this role do NOT offer visa sponsorship.

Experience Requirements & Qualifications

Core Experience

• 4+ years in Python backend development, including building production
  APIs/services and/or worker-based processing systems.
• Demonstrable experience in implementing Generative AI, AI/LLM-enabled features
  or systems (agentic workflows, RAG, tool calling, evaluation/monitoring) is preferred.
• Strong capability in backend fundamentals: service boundaries, API contracts, async
  execution, retries/idempotency, error handling, and performance optimization.
• Advanced Python engineering skills: clean architecture, modularity, testability,
  packaging, secure coding, and maintainability at team scale.
• Strong experience building API-first services (FastAPI or equivalent), RESTFul APIs
  including auth patterns (OAuth2/JWT/API keys), versioning, and backwards
  compatibility.
• Integrate and manage relational and vector databases.
• Strong schema/data contract practice using typed models and validation (e.g.,
  Pydantic-style patterns), including strict structured outputs and schema evolution.
• Working with version control tools like GitHub (branching, PR reviews, release
  tagging, CI-friendly workflows).
• Strong experience with context grounding methods, and context engineering when
  working with LLMs (RAG, evidence capture, context selection, prompt/context
  structuring).
• Experience using automation tools and integrating with external applications (API-
  based integrations, workflow triggers/actions, third-party systems).
• Experience building integration-heavy systems: consuming/producing APIs, handling
  enterprise data formats, and creating maintainable connectors.
• Working knowledge of distributed execution patterns: background jobs, scheduling,
  worker pools, and stateful workflows.
• Ability to work with ambiguity, break down requirements, and deliver reliably with
  strong ownership and communication.

Nice to Have

• Experience with agent orchestration frameworks (e.g., LangGraph-like patterns) and
  LLM observability/evaluation tools (Langfuse-like capabilities).
• Experience integrating enterprise-hosted LLMs (including vertex AI / managed
  equivalents) and working with provider-agnostic abstraction layers (routing, fallback,
  cost-aware selection).
• Experience with job queues, distributed tracing, dashboards/alerts, and runbook-
  driven operational practices.
• Experience supporting regulated enterprise delivery: audit-friendly logging, change
  controls, secure configuration, and controlled deployments.
• Platform/DevOps awareness (preference): Docker basics; Kubernetes/OpenShift
  fundamentals; logging/monitoring patterns; secrets management and environment
  separation (DEV/UAT/PROD).

Main Tasks and Responsibilities

1) Build Python Services and Agentic Components

• Develop production-grade backend services and worker processes aligned to the
  defined solution architecture.
• Implement orchestration components: job queues, scheduling, state
  management/state machines, retries, idempotency, and compensating actions.
• Build and maintain tool connectors/integrations with enterprise systems (APIs,
  databases, files), following safe execution boundaries and permission controls.
• Contribute reusable libraries and shared components to accelerate delivery across
  multiple client solutions.

2) Implement Reliable LLM and RAG Capabilities

• Integrate LLM capabilities into services using tool/function calling, structured
  outputs, and strict schema validation.
• Develop and maintain RAG pipelines: ingestion, indexing, retrieval, grounding, and
  evidence capture/citations where required.
• Apply context engineering practices: selecting/structuring context, minimizing
  irrelevant context, maintaining traceability, and improving response determinism.
• Implement guardrails and safety controls: input validation/sanitization, output
  validation, refusal/fallback handling, and policy-aligned tool usage.

3) Testing, Quality, and Release Discipline

• Build and maintain test suites: unit, integration, and regression testing (including
  prompt/agent regression where applicable).
• Participate in code reviews and follow engineering standards for maintainability,
  security, and correctness.
• Use GitHub-based workflows effectively: PR hygiene, branching strategies, code
  owner reviews, and CI/CD integration.
• Support release processes with strong documentation, configuration discipline, and
  readiness checks.

4) Observability, Performance, and Operational Readiness

• Implement logging, tracing, metrics, and decision logs for services and agent runs;
  support run replay and incident investigation.
• Profile performance bottlenecks and optimize latency, throughput, and cost across
  critical paths.
• Contribute to dashboards, alerts, runbooks, and operational procedures to maintain
  stable production systems.

5) Security, Compliance, and Enterprise Delivery

• Implement secure coding practices, secrets handling, and least-privilege patterns in
  tool execution and integrations.
• Follow enterprise governance expectations: audit-friendly logs, change controls,
  environment separation, and controlled deployments.
• Collaborate closely with the Agentic AI Architect, infra Teams, COE to deliver
  compliant, production-ready solutions .