AI Engineer

Fintricity is an AI-first consulting and technology firm helping enterprises transform the way they use data, artificial intelligence, and digital platforms. Founded from a long-standing background in financial technology, data engineering, analytics, and digital transformation, Fintricity works with organisations to move from strategy to production-ready solutions. Its approach combines business consulting, architecture, software engineering, data science, agile delivery, and change management to help clients build practical, scalable AI and data capabilities.

Kendra Labs is Fintricity’s technology and innovation venture, focused on building enterprise-grade AI, data, and agentic infrastructure. The platform helps organisations bring together fragmented data, create trusted AI-ready knowledge layers, deploy intelligent agents, and govern AI solutions at scale. Kendra Labs is designed for businesses that want to become AI-first: using data, automation, and agentic systems to improve decision-making, operational efficiency, and product innovation.

Together, Fintricity and Kendra Labs combine deep consulting expertise with proprietary AI platform capability. We work at the intersection of strategy, engineering, data, and applied AI, helping clients design, build, and deploy intelligent solutions that deliver measurable business outcomes. Our teams operate with an AI-first mindset, using modern tools, automation, and agentic workflows to accelerate delivery while maintaining strong governance, security, and enterprise readiness.

We are looking for people who want to work on meaningful AI and data transformation challenges, contribute to cutting-edge agentic technology, and help organisations move confidently into the next generation of intelligent enterprise systems.

Job Description

Fintricity and Kendra Labs are building enterprise-grade AI infrastructure for the next generation of agentic systems. Our work spans AI gateways, model orchestration, MCP/tool gateways, agent control planes, identity, governance, security, data platforms, code intelligence, and production-grade AI automation.

We are looking for an AI Engineer who can design, build, evaluate, and operate reliable AI systems in real-world enterprise environments. You will work across Fintricity and Kendra Labs to turn frontier AI capability into robust products, internal platforms, customer-facing solutions, and repeatable engineering patterns.

This role is ideal for an engineer who combines strong software engineering fundamentals with hands-on experience in LLMs, agents, retrieval, evaluation, observability, and secure production deployment.

### Responsibilities

* Design, build, and maintain AI-powered applications, agents, workflows, and platform components across Fintricity and Kendra Labs.
* Develop production-grade LLM and agentic systems using modern AI engineering patterns, including tool calling, retrieval, orchestration, memory, evaluation, and human-in-the-loop controls.
* Build integrations with enterprise systems, APIs, data sources, model providers, vector stores, code repositories, and MCP-compatible tools.
* Contribute to core Kendra Fabric modules, including AI gateway, agent control plane, MCP/tool gateway, code graph, data plane, identity, security, and governance capabilities.
* Implement robust evaluation pipelines for model quality, agent behaviour, latency, cost, reliability, and safety.
* Design and improve prompt, context, and workflow patterns for repeatable enterprise use cases.
* Build observability, tracing, logging, and debugging capabilities for AI systems in development and production.
* Work with product, engineering, customer, and leadership teams to convert ambiguous business problems into practical AI solutions.
* Apply secure engineering practices for authentication, authorization, data handling, auditability, model access, and tool execution.
* Prototype rapidly, validate assumptions with evidence, and harden successful prototypes into maintainable production systems.
* Document architecture, design decisions, technical trade-offs, and operational runbooks clearly.

### Requirements

- Strong software engineering experience in Python, TypeScript, or both.
- Experience with Claude, Gemini, Antigravity, and similar systems to build enterprise applications.
- Practical experience building with LLMs, AI APIs, agent frameworks, RAG systems, tool calling, or workflow orchestration.
- Understanding of modern AI system design: context engineering, retrieval, embeddings, vector databases, structured outputs, function calling, evaluations, guardrails, and observability.
- Experience designing and consuming APIs, working with cloud services, and deploying production systems.
- Ability to reason about reliability, latency, cost, security, privacy, and maintainability in AI applications.
- Strong debugging skills across application code, model behaviour, data pipelines, prompts, and external integrations.
- Familiarity with Git-based development, CI/CD, testing, code review, and engineering documentation.
- Comfortable working in a fast-moving environment where product direction, technical architecture, and customer needs evolve quickly.
- Clear written and verbal communication, with the ability to explain complex AI and engineering concepts to technical and non-technical stakeholders.
- Evidence of ownership: you can take a problem from discovery through design, implementation, testing, deployment, and iteration.

### Nice to Haves

- Experience with MCP, agent platforms, tool gateways, AI gateways, model routers, or multi-provider LLM infrastructure.
- Experience with LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, OpenAI Assistants/Responses APIs, Anthropic Claude, Gemini, or comparable frameworks and APIs.
- Experience building enterprise AI, governance, security, compliance, or regulated-industry systems.
- Experience with knowledge graphs, code intelligence, repo analysis, semantic search, or large-codebase understanding.
- Experience with observability tools such as OpenTelemetry, LangSmith, Arize/Phoenix, Helicone, Portkey, or similar platforms.
- Experience with vector databases and search systems such as pgvector, Qdrant, Weaviate, Pinecone, OpenSearch, Elasticsearch, or Vespa.
- Experience with cloud platforms such as AWS, Azure, GCP, Vercel, Cloudflare, or Kubernetes-based environments.
- Experience with identity, access control, SSO, RBAC/ABAC, audit logs, secrets management, or secure tool execution.
- Contributions to open-source AI, developer tooling, infrastructure, or automation projects.
- Prior experience in consulting, product engineering, startup environments, or customer-facing technical delivery.

### What Success Looks Like

- You ship useful AI systems that move from prototype to production.
- You make AI behaviour measurable, observable, and improvable.
- You reduce ambiguity by creating clear technical plans, tests, evaluations, and documentation.
- You help establish reusable engineering patterns for Fintricity and Kendra Labs.
- You balance speed with reliability, security, and long-term maintainability

Qualifications

BSc/BEng, Master, or PHD in a Science Subject, including Computer Science, Electronics, Electronic Engineering or similar.

Additional Information

"All your information will be kept confidential according to EEO guidelines".

Indvidual has to be a UK PAYE, and not in other countries.