Senior AI Software Engineer

Centrica plc; Windsor; Hybrid; CAD 159,000 - 225,000; Full time

Job summary

Centrica plc in the UK is seeking a Senior AI Engineer to own the intelligence layer of our agentic products across voice, chat and email channels. You will design and deploy advanced AI services with Bedrock, AgentCore Runtime, and retrieval-augmented generation, balancing performance, safety and governance.

You will implement multi-step orchestration, manage conversational state, and measure accuracy and containment with a formal evaluation harness, working with Terraform, CI/CD and cloud

Qualifications

• Substantial software engineering experience with strong Python.
• Proven delivery of LLM/agentic systems to production with real users.
• Hands-on experience with tool and function calling, multi-step agent orchestration, and managing conversational state across turns.
• Experience with retrieval-augmented generation and evaluation harness.
• Cloud AI services deployment using serverless or containers and IaC like Terraform, with CI/CD.

Responsibilities

• Design and build agentic services on Bedrock and AgentCore Runtime.
• Implement multi-step orchestration, state and memory management, and failure handling.
• Develop retrieval-augmented generation with content prep, embedding strategy, and grounding.
• Define evaluation metrics (accuracy, containment, safety) and governance reviews.
• Ensure latency, cost, and guardrails; monitor in production with observability tools.
• Document build-vs-buy decisions and architecture records for governance.

Skills

Python

LLM/agentic systems

Production code

Multi-step orchestration

Cloud deployments

CI/CD

Education

Bachelor's degree in CS/Engineering

Tools

Bedrock

AgentCore Runtime

Terraform

CI/CD pipelines

Job description

Senior AI Engineer

Join us, be part of more. We’re so much more than an energy company. We’re a family of brands revolutionising how we power the planet. We're energisers. One team of 21,000 colleagues that energise a greener, fairer future by creating an energy system that doesn’t rely on fossil fuels, whilst living our powerful commitment to igniting positive change in our communities. Here, you can find more purpose, more passion, and more potential. That’s why working here is #MoreThanACareer. We do energy differently – we do it all. We make it, store it, move it, sell it, and mend it.An opportunity to play your part.

Location: UK-based hybrid role, occasional travel to site.

You will join the Channel Platforms team, which owns Centrica’s customer conversation estate across voice, chat and email. Our agents are already live: a pay‑as‑you‑go voice assistant built on a real‑time speech‑to‑speech model, a multi‑channel customer‑facing agent spanning voice, chat and WhatsApp, and a standalone agentic webchat service running on AgentCore Runtime. As a Senior AI Engineer you will own the intelligence layer of these products. That means the agent’s reasoning and orchestration, the tools it can call, the knowledge it retrieves, the guardrails that constrain it, and the evaluation that proves it works. You will decide what good looks like and then build the measurement to show whether we are hitting it. This is a role for someone who has taken generative AI past the demo stage. Our problems are the hard ones that show up after launch: grounding answers in the right source, keeping voice latency low enough to feel natural, handing off to a human at the right moment, and proving to a governance board that the system behaves safely at scale. You do not need a contact centre background. If you are a strong engineer who has built real agentic systems on Bedrock or a comparable platform, we will teach you the domain.

Day to day responsibilities

Design and build agentic services on Amazon Bedrock and AgentCore Runtime, including multi‑step orchestration, state and memory management, and graceful handling of failure and ambiguity. Build and own the tool layer that agents act through, exposing business capability such as account lookup, billing and identity as reliable, well‑described actions with sensible error semantics. Build retrieval‑augmented generation over Bedrock Knowledge Bases, taking ownership of content preparation, chunking, embedding strategy, retrieval quality and answer grounding. Treat prompts as engineering artefacts. Version them, test them, and understand precisely what changed when behaviour shifts. Build the evaluation harness. Define what accuracy, containment and safe behaviour mean for each product, assemble regression sets, and make it possible to ship a prompt or model change with evidence rather than optimism. Implement guardrails and safety controls, including PII handling, out‑of‑scope refusal, jailbreak resistance and clean escalation to a human advisor. Engineer for latency and cost. Streaming, model selection and routing, caching and token budgets all matter, and in voice they matter in real time where a pause of a few hundred milliseconds is felt by the customer. Instrument agent behaviour end to end so that tool failures, retrieval misses and abandoned conversations are visible in Datadog and QuickSight, not discovered through complaints. Support live AI services in production, investigating incidents and diagnosing non‑deterministic behaviour, then closing the loop with permanent fixes. Make and document build versus buy calls, writing Architecture Decision Records and taking designs through our AI governance board and change control process. Track how the generative AI landscape is moving and bring the parts that deliver clear benefit into the roadmap, with an honest read on the risk as well as the upside.

What we need from you

Substantial software engineering experience, with strong Python. You write production code, not just notebooks. Demonstrable delivery of LLM or agentic systems into production for real users, and the scar tissue that comes with it. We will ask what broke and what you changed. Hands‑on experience with tool and function calling, multi‑step agent orchestration, and managing conversational state across turns. Production experience with retrieval‑augmented generation, including retrieval quality evaluation and answer grounding, not just wiring up a vector store. A rigorous approach to evaluation. You have built or owned an eval harness, defined the metrics that mattered, and used them to make release decisions. Experience deploying and operating AI services in the cloud using serverless or container‑based architectures, infrastructure as code such as Terraform, and CI/CD. Practical application of responsible AI, covering data privacy, guardrails, bias, explainability and human‑in‑the‑loop design, in an environment with real compliance obligations. Ability to debug systems that are non‑deterministic by nature, and the patience to reason about behaviour statistically rather than case by case. Strong collaboration and communication skills. You can explain to a product owner why the agent got something wrong, and to a governance board why the controls are sufficient. Degree in Computer Science, Engineering or a related discipline, or equivalent practical experience.

Desirable: Amazon Bedrock specifically, including AgentCore Runtime, Bedrock Knowledge Bases and Bedrock Guardrails. If your experience is on another foundation model platform, that transfers. Voice AI experience, including speech‑to‑speech models, real‑time streaming, barge‑in and turn‑taking, or ASR and TTS pipelines. Any contact centre exposure, including Amazon Connect, is a bonus rather than an expectation. Familiarity with agent interoperability standards such as the Model Context Protocol. Experience with fine‑tuning, distillation or model routing where it materially moved cost or latency. Delivery in a regulated industry, including working with vulnerable customer requirements or comparable obligations. AWS certification at Associate level or above.

Core competencies & technical skills (AI and emerging technology)

Designs, integrates and operates AI‑enabled solutions within enterprise environments, including prompt‑driven workflows, retrieval‑augmented systems and AI agents. Applies structured evaluation, testing and monitoring practices to ensure AI outputs are reliable, secure and compliant with organisational guardrails. Prepares and manages data used in AI workflows and takes responsibility for the responsible lifecycle of AI features from experimentation through deployment and continuous improvement. Demonstrates the safe and responsible use of AI tools, with clear knowledge of when AI use is appropriate and strong awareness of accuracy, bias and compliance. Brings the ability to design and reuse prompt templates to support consistent, high‑quality workflow outputs, and skilled in using AI to triage, classify and analyse information within Centrica policy guardrails. Strong ability to recognise higher‑risk scenarios and escalates to governance or security as needed. Alongside this, showing proficiency in enterprise AI co‑pilots, knowledge assistants and AI‑enhanced productivity tools.

Why should you apply?

We’re not a perfect place – but we’re a people place. Our priority is supporting all of the different realities our people face. Life is about so much more than work. We get it. That’s why we’ve designed our total rewards to give you the flexibility to choose what you need, when you need it, making sure that you and your family are supported not only financially, but physically and emotionally too. When it comes to energy, no one does it like us. We make it, store it, move it, sell it and mend it. We’re made up of 12 different businesses, but united by our purpose as Centrica. Through innovative green products, intelligent energy solutions and developing smarter ways to use and save energy, we’re not just part of the energy transition, we’re leading it. We offer a UK’s best Carers Policy, a supportive approach to flexibility and wellbeing, and a culture where differences are celebrated and everyone can belong. Your growth is non‑negotiable, and your ambitions are our priority.