Machine Learning Engineer

Intelix.AI; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Intelix.AI in the United Kingdom seeks hands-on AI/ML engineers to develop production-grade AI products in a regulated financial services environment. This role focuses on building, testing and operating enterprise AI solutions, not just notebooks or prototypes.

You will design and deploy production AI applications, develop RAG pipelines, integrate with data platforms, and ensure observability, security and governance. A hybrid model with largely remote work in London or Manchester is offered.

Qualifications

• Must have hands-on AI/ML engineering experience in production environments.
• Experience building production AI/ML systems, not just research.
• Familiarity with enterprise risk, observability, and governance for financial services.

Responsibilities

• Design and build production AI, Generative AI and Agentic AI applications.
• Develop RAG pipelines, agent workflows and enterprise knowledge integrations.
• Build APIs and services using Python and modern backend frameworks.
• Integrate AI capabilities with internal systems, data platforms and business workflows.
• Implement evaluation, observability, guardrails and human-in-the-loop controls.
• Support deployment through CI/CD, cloud platforms and containerised environments.
• Work with Product Managers, Technical BAs, Architects, Data and Risk teams.
• Contribute to testing, operational readiness and production support.
• Strong Python and software-engineering capability.
• Production AI/ML, LLM, RAG or Agentic AI delivery experience.
• Experience with APIs, microservices and system integration.
• Cloud experience across Azure, AWS or GCP.
• Familiarity with LangGraph, LangChain, Semantic Kernel, AutoGen or similar frameworks.
• Understanding of model evaluation, observability, security and governance.
• Private-sector delivery experience is essential.
• Banking or financial-services experience is highly desirable.

Skills

Python

Software engineering

LLM/AIA delivery

APIs & microservices

Cloud platforms

Observability & governance

Security & risk

Regulated financial services

Production-grade deployment

Tools

LangGraph

LangChain

Semantic Kernel

AutoGen

Git & CI/CD

Docker

Kubernetes

Azure/AWS/GCP

Job description

Generative AI | Agentic AI | RAG | Production Engineering

Initial 3 months + extension | London or Manchester | Hybrid - Largely Remote

We are looking for hands‑on AI/ML Engineers to support the development of production‑grade AI products within a major regulated financial‑services environment.

The requirement is for engineers who can build, integrate, test and operate enterprise AI solutions not candidates whose experience is limited to research, notebooks or chatbot prototypes.

Key responsibilities

• Design and build production AI, Generative AI and Agentic AI applications
• Develop RAG pipelines, agent workflows and enterprise knowledge integrations
• Build APIs and services using Python and modern backend frameworks
• Integrate AI capabilities with internal systems, data platforms and business workflows
• Implement evaluation, observability, guardrails and human‑in‑the‑loop controls
• Support deployment through CI/CD, cloud platforms and containerised environments
• Work closely with Product Managers, Technical BAs, Architects, Data and Risk teams
• Contribute to testing, operational readiness and production support
• Strong Python and software‑engineering capability
• Production AI/ML, LLM, RAG or Agentic AI delivery experience
• Experience with APIs, microservices and system integration
• Cloud experience across Azure, AWS or GCP
• Familiarity with frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen or similar
• Understanding of model evaluation, observability, security and governance
• Private‑sector delivery experience is essential
• Banking or financial‑services experience is highly desirable

We are particularly interested in engineers who understand that production AI requires reliability, monitoring, controls and operational ownership not just model accuracy.