• Explore data and real-world cases to develop signals and machine learning models that identify and reduce platform risk. • Partner with cross-functional teams to design practical ML solutions that fit real workflows. • Analyze and prepare training data, including working with crowdsourced labeling and human-in-the-loop processes. • Prototype, train, and iterate on machine learning models, using a mix of established and novel techniques. • Own the path from idea to production: deploy ML and signal-based models, and write backend code when needed. • Build observability into models, debug performance gaps, and continuously improve based on real-world results. • Measure impact using offline evaluation and experiments such as A/B tests.

Company Description Hi! We're UW. We’re on a mission to take the headache out of utilities by providing them all in one place. One bill for energy, broadband, mobile and insurance and a whole lot of savings! We’re aiming to double in size as we help more people to stop wasting time and money. Big ambitions, to be delivered by people like you. Got your attention? Read on.. We put people first.. it's all about you! We are seeking a production-focused Machine Learning Engineer to bridge the gap between data science research and scalable, reliable software. In this role, you will partner with Data Scientists to re-architect experimental models (POCs)—such as Next Best Action and Churn Propensity—for production. You will own "Day 2" operations including deployment, latency optimization, and monitoring, while also building the infrastructure for GenAI and RAG applications powering our tools. Job Description We Deliver Progress.. What you'll do and how you'll make an impact.. As a Machine Learning Engineer at UW, your responsibilities will include: - Predictive Modelling: Design and deploy robust ML models to solve business challenges, specifically Churn Propensity and Next Best Action (NBA) engines. - Customer Analytics: Develop advanced Customer Segmentation using clustering techniques to tailor services and communications. - Commercial Valuation: Own xLTV and ROI logic, modeling long-term customer value to optimize acquisition and retention spend. - Deployment & Ops: Collaborate with Data Engineers to productionise scalable models, ensuring continuous monitoring for drift and performance. - Experimentation: Design and analyse A/B tests to validate model effectiveness and measure commercial uplift. - Stakeholder Partnership: Translate complex statistical outputs into actionable insights for Marketing, Product, Commercial and Ops stakeholders. Qualifications We put people first. It’s all about you.. - Technical Mastery: - Production ML Experience: Proven experience deploying Machine Learning models into high-traffic production environments (retail, fintech, or utilities experience is a plus). - Tech Stack: Strong proficiency in Python and software engineering best practices (unit testing, modular code, Git). Experience with containerization (Docker, Kubernetes) is essential. - MLOps Tooling: Experience with model registries and monitoring tools (e.g., MLflow, Grafana). - Desirables: Experience with Feature Stores (e.g., Feast, Tecton). Knowledge of streaming data technologies (Kafka, Pyspark). Hands-on experience building or deploying LLM-based applications, specifically working with RAG architectures and vector databases. - Impact & Scope: - You have a track record of leading high-impact initiatives that align with company strategy. You can evaluate proposed work against team goals and provide critical feedback to ensure value delivery. - Planning & Delivery: - You are capable of independently implementing small to medium sized features through to completion. - Operational Excellence: - Continuous improvement mindset: Identify process gaps and proactively propose solutions, seeking out feedback from your team. - Business & Domain Knowledge: - Experience in working in a relevant consumer-centric domain - Can advise stakeholders on how Machine Learning Engineering can be applied to solve business problems - Leadership & Culture: - Collaboration: A "Software Engineering mindset" with the ability to work empathetically with Data Scientists, understanding their workflows while enforcing production standards. Skills / Competencies - Strategic Problem Solving: Ability to break down vague, high-level business requirements into concrete, scalable technical architectures. - Clear Communication: Excellent verbal and written skills, with the ability to influence technical and non-technical audiences. - Accountability: Willingness to take ownership of critical systems and participate in on-call rotations. - Continuous Learning: Proactively seeking out the latest industry trends and introducing relevant innovations to the team. Don’t worry if you don’t have the whole list. If you feel you have most of it and can learn the rest pretty quickly then please don’t hesitate to apply. Overall we are looking for imaginative and pragmatic problem-solvers who want to help make a positive impact with data at UW. Additional Information We’ve got big ambitions so there’s going to be plenty of challenges. There are also a lot of benefits: - An industry benchmarked salary. We’ll share it during your first conversation. - Discretionary performance bonus - Flexible remote working with the option to visit our London hubs for team meetings and company events. You'll have access to a range of hot desking spaces around the UK - Electric Car salary sacrifice scheme through Tusker - Discount on our services and you get our coveted Cashback Card for free. You’ll also get access to 100s of rewards and discounts through Perkbox - A matched contribution pension scheme and life assurance up to 4x your salary. You can also access free mortgage advice and a financial wellbeing tool. - Family-friendly policies, designed to help you and your family thrive. - Discounted private health insurance, access to an Employee Assistance line and a free Virtual GP. Our wellbeing app Unmind supports your mental health. - Belonging groups that help UW shape an even more inclusive future. - A commitment to helping you develop your career journey through learning, coaching and new experiences Not sure you meet all the requirements? Let us decide! Research shows that women and members of other underrepresented groups tend not to apply for jobs if they think they may not meet every qualification, when in fact they often do. We provide equal opportunities, a diverse and inclusive work environment, and fairness for everyone. You are welcome to apply no matter your age, disability, gender, marriage or civil partnership status, pregnancy and maternity status, race, religion or belief, or sexual orientation. Please don’t be afraid to ask about what we can do to support your needs. All requests will be carefully and fairly considered. Please note, if you are successful and offered a role at UW, you will be subject to a background check. Where checks are unsatisfactory or incomplete and/or a failure to reveal information relating to convictions that you are required to identify as part of the background checks, could lead to withdrawal of an offer of employment. - Employment Status: Full-Time

Overview Nakupuna Solutions is seeking an AI/ML Engineer with strong experience delivering end-to-end machine learning systems in Palantir Foundry. This role is responsible for designing, building, deploying, and operating AI/ML solutions in production environments. The AI/ML Engineer works closely with data scientists, data engineers, and business stakeholders to ensure models are scalable, reliable, and aligned with organizational goals, turning advanced analytics into operational capabilities that deliver measurable impact. Responsibilities The following reflects management’s definition of essential functions for this job but does not restrict the tasks that may be assigned. - Lead full lifecycle AI/ML system development in Palantir Foundry, including data pipeline integration, feature engineering, model implementation, deployment, and production monitoring - Design, develop, and optimize machine learning models and algorithms for performance, scalability, and reliability - Collaborate with data scientists and engineers to transition models from research and experimentation into production systems - Build and maintain model deployment, versioning, and monitoring workflows - Integrate AI/ML solutions into existing platforms and business processes - Evaluate and apply emerging AI/ML technologies where they provide clear business value - Ensure robustness, maintainability, and performance of deployed AI/ML systems in operational environments Qualifications Skills/Qualifications: - 5+ years of hands-on experience building, deploying, and maintaining AI/ML systems end-to-end in production environments - Proficiency in programming languages such as Python, R, or Java - Expertise in AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) - Experience delivering AI/ML solutions in Palantir Foundry (e.g., Code Workbooks, Code Authoring, pipelines, model operationalization) - Strong understanding of machine learning techniques, including supervised and unsupervised learning and deep learning approaches - Experience with model deployment, monitoring, and MLOps tooling (e.g., Docker, Kubernetes, MLflow or similar) - Experience working with large-scale data systems and cloud or hybrid environments - Strong analytical and problem-solving skills, with the ability to work with complex systems and datasets - Excellent communication skills to explain technical concepts to non-technical stakeholders and collaborate across teams Education/Experience: - 5+ years of hands-on experience delivering AI/ML solutions end-to-end in production environments, including data ingestion, feature engineering, model development, deployment, and ongoing monitoring - 3+ years of practical experience building and operationalizing AI/ML systems in Palantir Foundry (e.g., Code Workbooks, Code Authoring, pipelines, model operationalization) - Advanced degree in Artificial Intelligence, Machine Learning, Data Science, or related fields Clearance Requirements: Must be a U.S. Citizen. Secret clearance required. Physical Requirements: The ideal candidate must at a minimum be able to meet the following physical requirements of the job with or without a reasonable accommodation: - Ability to perform repetitive motions with the hands, wrists, and fingers. - Ability to engage in and follow audible communications in emergency situations. - Ability to sit for prolonged periods at a desk and working on a computer. The Nakupuna Companies use a market-based compensation strategy to ensure that our employees are compensated within applicable market ranges commensurate with multiple factors, including but not limited to the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability, organizational requirements, and position location. The projected compensation range for this position is $170,000.00 to $210,000.00 (annualized USD). The salary range displayed represents the typical salary range for this position and is just one component of Nakupuna Companies total compensation package for employees.

• Design and Develop machine learning infrastructure, tooling, and models to help teams deliver world class experiences. • Help product and development teams understand the data lifecycle and the inherent experimental nature of machine learning. • Build internal products and platforms to enable teams to incorporate AI into their features and customer facing products. • Consult with teams to help them understand common patterns, anti-patterns, and tradeoffs of machine learning. Guide them through creating excellent customer experiences end to end. • Build scalable, resilient services to support data integration, event processing, and platform extensions. • Contribute to the continued evolution of product functionality that services large amounts of data and traffic. • Write code that is high-quality, performant, sustainable, and testable while holding yourself accountable for the quality of the code you produce. • Coach and collaborate inside and outside the team. You enjoy working closely with others - helping them grow by sharing expertise and encouraging best practices. • Work in a cloud environment, considering the implementation of functionality through several distributed components and services. • Work with our stakeholders to translate product goals into actionable engineering plans.