{"id":3090622,"url":"https://alion.io/job/tesco-applied-machine-learning-engineer-sde-2","title":"Applied Machine Learning Engineer - SDE 2","company":{"id":1059,"name":"Tesco","domain":"tesco.com","url":"https://alion.io/company/tesco","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":100,"open_postings":19,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":8,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Feature Store","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Azure","optional":true},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true},{"name":"Microsoft Entra ID","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-22T14:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-22T14:35:30Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"We are looking for a highly skilled Applied Machine Learning Engineer who combines strong mathematical foundations, software engineering excellence, and expertise in building scalable analytical systems. This role is not focused on ad hoc analytics, notebook-based experimentation, or model development in isolation. We are seeking engineers who can design, develop, operationalise, and scale statistical and machine learning solutions that solve complex business problems in production environments. You will work at the intersection of software engineering, machine learning, forecasting, optimisation, and big data technologies. Success in this role requires a deep understanding of mathematics, statistics, machine learning, and forecasting methodologies, along with the engineering discipline required to transform analytical prototypes into reliable, maintainable, and scalable production systems.\n\nWe are particularly interested in candidates with experience supporting forecasting, planning, optimisation, or decision intelligence platforms in Supply Chain, Retail, Logistics, Manufacturing, or related domains. The ideal candidate is equally comfortable discussing statistical assumptions, forecasting accuracy, optimisation constraints, software architecture, distributed processing, feature engineering, and production reliability.\n\nThe candidate will have responsibilities across the following functions: \n\nStatistical Modelling and Machine Learning: \nDesign, develop, and improve statistical and machine learning models for forecasting, planning, optimisation, and decision-support solutions.\nApply both classical statistical techniques and modern machine learning approaches to solve business problems.\nSelect appropriate modelling methodologies based on business requirements, data characteristics, and operational constraints.\nEvaluate model performance using rigorous statistical, operational, and business metrics.\nDevelop robust approaches for uncertainty estimation, model validation, and performance measurement.\nContinuously improve model quality, stability, scalability, and business impact.\n\nForecasting and Optimisation Solutions. \nBuild forecasting solutions for demand planning, inventory management, replenishment, capacity planning, pricing, and operational decision-making.\nDevelop probabilistic forecasting solutions where uncertainty estimation is critical.\nDesign and implement optimisation models that support operational decision-making at scale.\nBalance model accuracy, interpretability, scalability, and operational usability.\n\nProduction Machine Learning Engineering: \nPartner closely with Data Scientists, Applied Scientists, Product Teams, and business stakeholders to translate analytical prototypes into production-grade solutions.\nCollaborate on model design, feature engineering, validation methodologies, and performance optimisation.\nConvert research-oriented or notebook-based implementations into scalable, reusable, and maintainable software systems.\nDesign and implement reusable training, evaluation, inference, and retraining frameworks.\nDrive engineering best practices for productionizing statistical, forecasting, optimisation, and machine learning solutions.\nEstablish standards that improve reproducibility, reliability, and maintainability of analytical systems.\n\nMachine Learning Lifecycle Management: \nBuild and maintain end-to-end machine learning workflows across training, validation, deployment, monitoring, and retraining stages.\nImplement robust model evaluation and validation processes.\nDevelop model monitoring solutions that identify performance degradation, drift, and operational issues.\nSupport experiment tracking, model versioning, and feature versioning.\nLeverage and contribute to ML lifecycle platforms and tooling, including MLflow, Feature Stores, model evaluation frameworks, and model monitoring solutions.\nPartner with platform and data engineering teams to improve ML platform capabilities and developer productivity.\n\nMachine Learning at Scale: \nDesign and build solutions capable of training and scoring models against large-scale datasets.\nOptimise feature engineering, model training, and inference workflows within distributed environments.\nBuild scalable architectures that support millions of predictions and large volumes of operational data.\nImprove computational efficiency and cost-effectiveness of machine learning workloads.\nBalance modelling sophistication with operational and performance constraints.\nOperate comfortably within modern big data ecosystems and distributed computing environments.\n\nSoftware Engineering Excellence: \nDesign and develop production-quality software using modern engineering practices.\nBuild reusable frameworks, SDKs, libraries, and shared components that support multiple modelling use cases.\nApply object-oriented design principles and architectural patterns to analytical systems.\nImplement comprehensive testing strategies for model and application code.\nContinuously improve maintainability, reliability, and extensibility of solutions.\n\nRequirements: \nBachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or a related discipline.\n6+ years of experience developing statistical, forecasting, optimisation, or machine learning solutions in production environments.\nStrong programming skills in Python with the ability to write clean, maintainable, and testable code.\nStrong foundation in Object-Oriented Analysis and Design (OOAD).\nExperience designing and building production-grade software systems.\nStrong understanding of software architecture and system design fundamentals.\n\nMathematical and Statistical Foundations: \nStrong foundation in:\nMathematics\nStatistics\nProbability Theory\nMachine Learning\nForecasting\nOptimization Techniques\nDeep understanding of:\nStatistical Inference\nHypothesis Testing\nProbability Distributions\nTime-Series Analysis\nForecast Error Measurement\nUncertainty Quantification\nStrong understanding of:\nFeature Engineering\nBias-Variance Tradeoffs\nRegularization Techniques\nEnsemble Methods\nExperimental Design\nModel Evaluation Methodologies\nAbility to reason about model assumptions, limitations, interpretability, and business applicability.\n\nMachine Learning Engineering: \nExperience producing statistical and machine learning models.\nExperience partnering closely with Data Scientists to operationalise analytical solutions.\nStrong understanding of the end-to-end machine learning lifecycle.\nExperience designing scalable training, inference, and model evaluation workflows.\nAbility to translate mathematical models into maintainable software systems.\n\nBig Data and Distributed Computing: \nExperience developing and scaling analytical workloads in distributed environments.\nExperience working within modern big data ecosystems.\nHands-on experience with PySpark.\nStrong understanding of distributed data processing concepts and computational tradeoffs.\nAbility to design solutions that balance model accuracy, scalability, and computational efficiency.\n\nSoftware Engineering Practices\nStrong understanding of:\nSOLID Principles\nSeparation of Concerns\nDependency Injection\nEncapsulation and Abstraction\nComposition over Inheritance\nExperience applying design patterns and architectural patterns appropriately.\n\nExperience with: \nCode Reviews\nUnit Testing\nIntegration Testing\nTest Automation\nCI/CD\nVersion Control\nRefactoring and Technical Debt Management\nPassion for clean code, engineering craftsmanship, and long-term maintainability.\n\nPreferred Qualifications: \nSupply Chain and Planning Domain Experience.\nExperience in Supply Chain, Retail, Manufacturing, Logistics, Planning Systems, or Operations platforms.\n\nExperience developing solutions for: \nDemand Forecasting\nInventory Optimization\nReplenishment Planning\nSupply Chain Planning\nPricing and Promotion Analytics\nCapacity Planning\nNetwork Optimization\n\nForecasting and Statistical Modelling: \nExperience with:\nSARIMAX\nExponential Smoothing\nState Space Models\nProphet\nHierarchical Forecasting\nIntermittent Demand Forecasting\nBayesian Forecasting\nProbabilistic Forecasting\n\nMachine Learning and Advanced Analytics: \nExperience with: \nML algorithms\nScikit-Learn\nEnsemble Methods\nDeep Learning\nTemporal Neural Networks\nLarge-Scale Feature Engineering\nExplainability and Model Interpretability Techniques\n\nOptimisation and Decision Science: \nExperience with:\nLinear Programming\nMixed Integer Programming\nConstraint Optimization\nHeuristic Optimization\nNetwork Optimization\nSimulation Modeling\nOperations Research Techniques\n\nML Platform and Lifecycle Management: \nHands-on experience with:\nMLflow\nFeature Stores\nModel Evaluation Frameworks\nExperiment Tracking Platforms\nModel Monitoring Solutions\nExperience implementing:\nModel Versioning\nFeature Versioning\nAutomated Model Validation\nDrift Detection\nModel Performance Monitoring\n\nPlatform and Engineering: \nExperience with Docker and Kubernetes.\nExperience building reusable frameworks, SDKs, or shared platform capabilities.\nExperience defining engineering standards and architectural best practices.\nFamiliarity with Domain-Driven Design (DDD).\nExperience working closely with Data Engineering teams to deploy analytical solutions in large-scale data ecosystems.\n\nWhat We Value \nWe value engineers who:\nPossess strong mathematical intuition and analytical rigour.\nUnderstand the assumptions behind models, not just the tools used to train them.\nCan explain why a model works and when it should not be used.\nBridge the gap between Data Science and Software Engineering.\nTransform prototypes into scalable and maintainable production systems.\nBuild reusable modelling frameworks rather than project-specific implementations.\nApply OOAD principles, SOLID principles, and design patterns effectively.\nWrite clean, readable, and maintainable code.\nThink beyond experimentation and focus on delivering business outcomes.\nBalance model accuracy, operational complexity, scalability, and cost.\nTake ownership of production systems throughout their lifecycle.\nContinuously improve architecture, engineering practices, and model reliability.\nSkills\nAzure AD, Azure PIM, Microsoft Entra ID, Privileged Identity Management","description_format":"text","description_chars":10243,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Retail & Shop Systems","Online Groceries"],"lifecycle":[{"event":"open","at":"2026-10-22T14:35:30Z"}],"visa":[],"liveness":{"score":89,"band":"hot","label":"Hiring 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