Serious about craft.
Generous with people.

About the role

An early-career role, in the first intake of our Junior Program. We hire on evidence rather than credentials: something you've built and run on a cloud platform, however small.

You'll pair with senior engineers on real client work from week one on AWS, covering Terraform, observability and migrations. Learn AWS deeply, with GCP and Azure experience also valued. You do real work early, and never alone: a senior engineer owns every engagement and reviews what you ship.

What you'll work on

In your first year, paired and supported, you can expect to:

• Write and review Terraform alongside a senior engineer, until infrastructure-as-code becomes a language you think in.
• Help stand up and tune observability: dashboards, alerts, and the hard-won difference between noise and signal.
• Shadow on-call before you ever carry a pager, then carry one when you and your lead agree you're ready.
• Take part in cloud migrations, including the unglamorous, essential work of making them safe.
• Learn AWS deeply, with GCP and Azure experience also valued, and enough of a second cloud platform to translate.

What we look for: the craft

• A genuine foundation in how computers and networks work.
• Hands-on exposure to AWS, or transferable experience from GCP or Azure: a free tier, a certification, an internship or a deployed project.
• Some scripting fluency: Python, Bash, Go, or similar.
• A first encounter with infrastructure as code on AWS, using Terraform, AWS CloudFormation or Pulumi.
• Something you've built and kept running, that you can walk us through: including the parts that broke.

What we look for: how you work

• AI as a force multiplier, from day one. You already use OpenAI tools, Claude, Cursor, GitHub Copilot or agents to write faster, debug harder and learn quicker.
• Comfort being uncertain in the open. We hire for the ability to say "I don't know yet" and then go and find out.
• Care for the people downstream. You can write a clear note and explain a problem to someone who isn't an engineer.
• Mistakes are part of the work: never the measure of you.

About the role

You've spent three to seven years building and running production cloud infrastructure on AWS, GCP or Azure. Our cloud delivery centres on AWS. You write Terraform that reads like prose. You've been on call, and you know what a P0 feels like at 2 a.m.

You'll own substantial pieces of the substrate work other teams depend on, and pair with associate engineers as you go.

What we look for: the craft

• Deep fluency in AWS, or transferable production experience on GCP or Azure with a willingness to build depth in AWS. Working knowledge of a second cloud is valuable.
• Infrastructure as code on AWS using Terraform, AWS CloudFormation, Pulumi or equivalent, both to deploy and to reason about the system.
• Strong opinions on observability: what to instrument, what to alert on, what to silence.
• Familiarity with security and compliance regimes (SOC 2, ISO 27001, RBI guidelines, equivalents).
• A track record of running production systems that someone is paying for.

What we look for: how you work

• AI as a force multiplier. You use OpenAI tools, Claude, Cursor, GitHub Copilot or agents to write IaC faster, debug harder and document better. We'll teach you the next layer together.
• Comfort being uncertain in public. We talk about what we don't know in standups. We don't pretend.
• Customer-grade communication. You can write a clear runbook and explain a P0 to a CTO without jargon.

About the role

You've run production systems that someone was paying for: carried a pager, sat a P0 bridge at 2 a.m., and written the postmortem the next morning. You know the difference between an alert that matters and one that just fires.

You'll operate what we build after launch across AWS cloud foundations, Databricks data platforms and OpenAI applications, alongside other customer technologies. Help turn reactive firefighting into a discipline: evals, drift detection, reversible deploys and runbooks that stay true.

What you'll work on

• Run reliability on AWS-led client platforms, alongside GCP and Azure estates: SLOs, error budgets, patching and incident response on a follow-the-sun rota.
• Operate the data and model planes, with Databricks and OpenAI in our core stack: pipeline freshness, data-quality SLAs, model drift, evaluation and safe retraining.
• Build AgentOps discipline: tool routing, guardrails, cost ceilings, replay, and human-escalation paths for autonomous and semi-autonomous agents.
• Automate the loop: AI-assisted RCA, runbook-as-code, gated auto-remediation for known patterns: so the same incident never costs twice.
• Write postmortems your engineers will actually read, and feed every one back into the evals and the detection.

What we look for: the craft

• Production SRE, DevOps or platform-ops experience on AWS, or transferable experience from GCP or Azure: reliability, cost, security and recovery.
• Fluency with AWS CloudWatch, OpenTelemetry, Prometheus, Grafana or equivalents, and incident practice you've actually lived.
• Exposure to at least one of DataOps or MLOps: pipeline reliability, model deployment, drift, or evaluation.
• Infrastructure-as-code and scripting (Terraform, Python, Bash, or similar): enough to automate rather than repeat.
• A track record of keeping something running that mattered, and improving it while it ran.

What we look for: how you work

• AI as a force multiplier. Use OpenAI and other AI tools for assisted RCA, log clustering and runbook drafting, then teach the rest of us.
• Calm under a P0, and honesty in the postmortem: blameless, specific, and shared.
• Customer-grade communication. You can explain an outage to a CTO without jargon and without spin.

About the role

An early-career role, in the first intake of our Junior Program. We hire on evidence rather than credentials: show us something you've built with modern AI and can explain.

You'll pair with senior engineers on retrieval pipelines, agents and evals using OpenAI, AWS and Databricks, alongside other model and data platforms. Learn the modern stack on work that ships. You do real work early, and never alone: a senior engineer owns every engagement and reviews what you ship.

What you'll work on

In your first year, paired and supported, you can expect to:

• Help build retrieval pipelines on AWS and Databricks, alongside other data platforms, including the data work that decides whether retrieval is useful.
• Write and maintain evaluations: because a system is only as trustworthy as the eval set standing behind it.
• Wire up agents and copilots against real customer workflows, and watch how they behave outside the demo.
• Help instrument AI systems in production: latency, cost, drift, and the many ways a model quietly fails.
• Learn the modern stack hands-on: OpenAI APIs and SDKs, AWS AI services, Databricks, model gateways, vector stores and agent frameworks, on work that ships.

What we look for: the craft

• Comfortable in Python: you read other people's code and write your own without copying it whole.
• A real understanding of how modern AI gets used: you've called an OpenAI or another LLM API, built retrieval or fine-tuned a model.
• A first encounter with OpenAI SDKs, AWS AI services or Databricks, or a comparable toolchain such as a vector database, LangGraph, the Anthropic SDK or another model gateway.
• Some instinct for evaluation: you've felt the discomfort of not knowing whether an output is good, and tried to measure it.
• Something you've built with AI you can demonstrate: including what broke and what you changed.

What we look for: how you work

• AI as a force multiplier, from day one. You use OpenAI and other AI tools to help build AI: code, evals and data labelling.
• Comfort with stochasticity. Models are random, and so is much of this work.
• Comfort being uncertain in the open. We hire for "I don't know yet: let me find out".
• Care for the people downstream, and the understanding that mistakes are part of the work.

About the role

You've been deep in machine-learning systems for three to seven years: fine-tuning, deploying, serving, evaluating, monitoring. You know the gap between a notebook that works and a system that survives week six.

You'll own substantial pieces of the AI shipped into production for customers who measure it, with AWS, OpenAI and Databricks central to our delivery approach. Work across other model and cloud platforms where the customer context calls for it, and pair with associate engineers as you go.

What we look for: the craft

• Production ML and AI engineering on AWS, using OpenAI and Databricks where they fit: model serving, evals, observability and retraining loops.
• Comfort across the modern stack: AWS services including Amazon Bedrock, OpenAI APIs and OpenAI Agents, Databricks, PyTorch, transformers, vector databases, LangGraph and the Anthropic SDK; model gateways including GCP Vertex AI, Azure AI and the Anthropic API; and embedding pipelines.
• Working knowledge of AWS AI services, with experience on GCP Vertex AI, Azure AI or other cloud AI services also valued.
• Strong opinions on evaluations. You believe a system is only as good as the eval set you've published.

What we look for: how you work

• You're already using OpenAI and other AI tools to build AI: multi-agent systems for evals, agentic code review and LLM-augmented data labelling. Show us how.
• Customer empathy. You can sit with a CIO and explain why a particular eval failed and what we'll change.
• Comfort with stochasticity. Models are random. So is the work.

About the role

An early-career role, in the first intake of our Junior Program. We hire on evidence rather than credentials: show us something you've built that moves, shapes, or makes sense of data, however small.

You'll pair with senior engineers on real pipelines, data models and contracts, with Databricks and AWS at the centre of our data work. Experience with other data platforms is welcome. You do real work early, and never alone: a senior engineer owns every engagement and reviews what you ship.

What you'll work on

In your first year, paired and supported, you can expect to:

• Build and maintain pipelines on Databricks and AWS alongside a senior engineer, and learn why a pipeline is not done when the bytes arrive.
• Help model data: turning raw operational tables into something an analyst, or an AI system, can rely on.
• Write the tests and checks behind data contracts, until "is this number right?" has an answer you trust.
• Take part in reconciliation: making the dashboard agree with the source system, and stay agreed.
• Learn a modern data stack hands-on, led by Databricks and AWS data services, alongside warehouses, transformation tools and orchestration, on work that ships.

What we look for: the craft

• Comfortable with SQL: joins, window functions, aggregations.
• Some Python fluency: enough to move and reshape data, not do it by hand.
• A real understanding of what a data pipeline is. You've built one and watched it run more than once.
• A first encounter with Databricks or AWS data services, or another modern data stack such as Snowflake, BigQuery, dbt, Airflow or Dagster.
• Something you've built that handles real data, that you can walk us through: including the messy parts.

What we look for: how you work

• AI as a force multiplier, from day one. You use OpenAI tools, Claude, Cursor, GitHub Copilot or agents to write SQL and Python faster.
• A feel for data as a product, not a side-effect: you care whether the numbers are right and documented.
• Comfort being uncertain in the open. We hire for "I don't know yet: let me find out".
• Care for the people downstream, and the understanding that mistakes are part of the work.

About the role

You've spent three to seven years building data infrastructure companies actually trust. You know a pipeline isn't done when the bytes arrive: it's done when the dashboard matches the source system, and stays matched.

You'll own substantial pieces of the work that turns operational data into something AI can build on, with Databricks and AWS central to our delivery. Bring experience from Snowflake, BigQuery and other data platforms too, and work alongside the Applied AI team.

What we look for: the craft

• Production fluency in modern data stacks: Databricks and AWS data services, alongside Snowflake, BigQuery, dbt, Airflow, Dagster, Spark, Kafka or equivalent tools. Depth in some, fluency in more.
• Cloud foundations on AWS, or transferable experience on GCP or Azure: IAM, networking, cost and recovery.
• Strong opinions on data contracts, observability, and documentation. You think of data as a product, not a side-effect.
• Willingness to work where the gap is: modeling one quarter, latency the next, cost the quarter after.

What we look for: how you work

• AI as a force multiplier. OpenAI and other LLM tools for assisted SQL, agentic ETL and semantic auto-mapping are tools you reach for, then teach the rest of us.
• Comfort being on the customer's side of the table. You'll often work embedded with a client team for weeks.
• The instinct to make data legible to humans downstream: engineers, analysts, executives. All of them.

About the role

This is not quota-led, pushy sales. The work being sold is backed by a delivery team that ships, no matter what. Your job is to understand the customer's real problem, shape the right engagement with our engineers, and let the work do most of the talking.

What we look for: the craft

• Six to twelve years in a customer-facing technical role: solutions or sales engineering, delivery lead, architect, or consulting in a serious technology environment. We're flexible on titles.
• Enough technical depth to scope an engagement with engineers and defend it to a CIO, with AWS for cloud, OpenAI for AI and Databricks for data central to our approach. Depth in one discipline matters; you don't have to be all three.
• Domain depth in at least one of: financial services, manufacturing, retail, healthcare, public sector, technology.
• Comfort co-pitching with engineers and our partners: AWS, OpenAI, Databricks and ElevenLabs. Understand customer estates that also use GCP, Azure, Snowflake or Anthropic. Share credit for the work.

What we look for: how you work

• Customer success first. The clearest signal we look for: a customer who'd take your call after a contract ends.
• AI as a force multiplier in your own work, led by OpenAI tools: deep research, structured discovery, proposal drafting and ROI modelling. Experience with other AI tools is valuable too.
• Honesty under pressure. We'll sometimes turn down work that isn't right for us. You'll tell the customer why, and earn more trust by doing it.