Satalia

• You will be responsible for creating an enterprise quality Optimisation and ML based solution • You will be involved in technology selection, API design, and establishing core patterns within our products and client projects • You will consider the functional and non-functional requirements to inform these decisions and lead our development team in implementing them • You will work with our world class Optimisation, Data Science and Data Engineering teams to provide robust, high quality, performant cloud based solutions for our customers • This is not a role for someone who doesn’t love development • Our engineers are expected to prove solutions and contribute to the development team, as well as reviewing code and driving improvement to our development practice and processes • At the same time, you need to be a great communicator: the role includes direct interaction with our clients and business stakeholders, which are a mixture of technical and non-technical audiences • Writing enterprise quality solutions, with extensive experience and familiarity with how enterprise software solutions fit into a wider technical landscape • Creating hybrid serverless and containerised solutions to execute optimisation, data science and ML models • Contributing to existing deployment pipelines and cloud IaC deployment packages • Understanding and relating customer/business needs to the technical solutions we create • Liaising with the customer, product owners and programme architects to ensure that the work we are doing is correct and architecturally sound • Making technology and approach choices to meet NFRs whilst delivering good value for money

• Designing and delivering enterprise-grade AI/ML and Data Science platforms within complex cloud ecosystems • Building serverless and containerised execution environments for ML and data workloads • Developing secure, scalable patterns for cloud Infrastructure-as-Code and deployments • Defining API standards, observability strategies and architectural guardrails • Establishing and evolving CI/CD pipelines with automation at the core • Leading and mentoring cross-functional engineering and data teams • Translating business requirements into pragmatic architectural solutions • Maintaining technical alignment with product, business and customer goals • Selecting technology approaches that meet non-functional requirements while optimising cost • Continuously researching emerging technologies and best practices and applying them where beneficial

• Working on key clients • Own day-to-day leadership for a portfolio of strategic existing clients in our logistics domain, ensuring delivery excellence and measurable value. • Drive retention and growth by identifying expansion opportunities, shaping account plans, and coordinating the right Satalia teams to deliver. • Partner closely with the Client Director on priorities, stakeholder mapping, quarterly business reviews, and commercial forecasting. • Anticipate risks and remove blockers early, maintaining high client satisfaction and long-term account health. • Developing partnership relationships with key clients • Build trusted-advisor relationships with senior stakeholders, moving engagements from project delivery to long-term partnership. • Proactively uncover client objectives and pain points, translating them into a roadmap of initiatives (including new use cases, pilots, and scaled rollouts). • Lead commercial conversations on renewals, scope evolution, and value articulation, working with the Client Director on pricing/contracting where needed.

• Developing complex cloud-native serverless applications • Writing and maintaining serverless functions in Typescript and Python • Utilising Serverless technology including API management, Serverless functions, Event driven architectures, Serverless databases and document stores • Implementing a range of integration patterns, from modern SaaS APIs using GraphQL to traditional FTP based integrations • Applying DevOps principles to empower teams to manage infrastructure directly using Git • Utilising Terraform for provisioning and managing cloud resources • Leveraging Design Patterns and reference architectures to design scalable and efficient cloud infrastructure • Having a strong test driven, quality first mindset to your work • Communicate effectively with excellent written and verbal skills • Familiarity with Diagrams-as-Code for documenting infrastructure architecture • Designing solutions observing cross-cutting concerns such as observability and system security • Taking ownership of deployments in a true Devops model

• Developing complex cloud-native serverless applications • Writing and maintaining serverless functions in Typescript and Python • Utilising Serverless technology including API management, Serverless functions, Event driven architectures, Serverless databases and document stores • Implementing a range of integration patterns, from modern SaaS APIs using GraphQL to traditional FTP based integrations • Applying DevOps principles to empower teams to manage infrastructure directly using Git • Utilising Terraform for provisioning and managing cloud resources • Leveraging Design Patterns and reference architectures to design scalable and efficient cloud infrastructure • Having a strong test driven, quality first mindset to your work • Communicate effectively with excellent written and verbal skills • Familiarity with Diagrams-as-Code for documenting infrastructure architecture • Designing solutions observing cross-cutting concerns such as observability and system security • Taking ownership of deployments in a true devops model

• Work with our solution architects to identify the best technologies and process for our clients • Act as a technical authority to engage and steer discussions on best practice and risk mitigation • Ensure security considerations are implemented, tested and maintained. • Build in Observability

• Deliver infrastructure solutions as part of an experienced cross-disciplinary team • Use hands-on experience to drive the design, development and deployment of Satalia’s Logistics and Delivery products • Identify the best technologies and process for clients with solution architects • Act as a technical authority to engage and steer discussions on best practice and risk mitigation • Ensure security considerations are implemented, tested and maintained • Build in Observability

• Build and iterate on ML models — from data exploration and feature engineering through training, evaluation, and deployment • Implement and maintain components of production ML pipelines: data pre-processing, model serving, monitoring, and retraining workflows • Contribute to LLM-powered systems — building prompt chains, evaluation harnesses, RAG pipelines, or fine-tuning workflows • Analyse large multimodal datasets (text, image, video, structured metadata) to extract features and insights that feed downstream models • Write clean, tested, production-quality Python code — not just notebooks • Participate in code reviews, design discussions, and experiment retrospectives

• Explore and prepare datasets — cleaning, feature engineering, and exploratory analysis across structured and unstructured data (text, image, tabular) • Train and evaluate ML models under the guidance of senior scientists, learning how to move from a working prototype to a production-ready system • Write and maintain Python code that runs in production — scripts, pipeline components, and data processing jobs — with support through code review • Help build and test components of LLM-powered systems: prompt templates, evaluation scripts, data loaders, and retrieval pipelines • Run experiments systematically: track hypotheses, log results, and communicate findings clearly to the team • Learn and adopt software engineering best practices — Git workflows, testing, documentation, and CI/CD — as part of your daily work

• Design and run training pipelines - data curation, model selection, hyperparameter search, ablation studies - and be accountable for model quality on live traffic • Build and maintain production inference services (latency budgets, batching strategies, quantisation, monitoring) that serve WPP's global client base • Architect agentic AI systems: define tool schemas, orchestration logic, evaluation criteria, and failure modes for multi-step LLM workflows • Work across the stack when needed - write the data pipeline, train the model, build the evaluation harness, deploy the service, and debug it when metrics drift • Set technical direction for your workstream - write design docs, make build-vs-buy decisions, and defend your approach with evidence • Mentor and set the quality standards for junior scientists