{"id":2622757,"url":"https://alion.io/job/dataxstream-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":4420637,"name":"Dataxstream","domain":"dataxstream.com","url":"https://alion.io/company/dataxstream","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"JavaScript","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"TypeScript","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Dagster","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"MLFlow","optional":true},{"name":"RAG","optional":true},{"name":"Recommender Systems","optional":true},{"name":"SAP HANA","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-10-01T00:00:00Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-18T04:21:39Z","board_verified":true,"closed_at":null,"days_open":17,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":17},"description":"We are looking for a Senior Machine Learning Engineer to help build the intelligence behind OMS+ IA. The focus is models that make a measurable difference inside enterprise order management: product and cross-sell recommendations, churn and propensity scoring, demand and delivery forecasting, and document intelligence.The role is split between product and delivery. On the product side, you will help design and build the models that ship as part of the platform. On the delivery side, you will work directly with customer data and customer teams to get those models performing in real environments, where the data is messierand the requirements are more specific than any roadmap anticipates.\nKey Responsibilities\nDesign, train, and evaluate models for prediction, ranking, and recommendation problems in order management. Things like cross-sell recommendations, churn and propensity scoring, and demand forecasting.\nContribute across the modeling lifecycle: problem framing, data preparation, feature engineering, training, evaluation, and retraining strategy.\nWork with product management to translate roadmap priorities into well-framed modeling problems.\nSupport delivery engagements: profile customer data, tune and validatemodels against it, and work with implementation and solution engineering teams to get results the customer can trust.\nHelp build reproducible training pipelines and experiment tracking in Python so results can be reviewed, rerun, and defended.\nProfile, clean, and validatelarge-scale enterprise SAP data, and be candid about what the data can and cannot support.\nPackage models and pipelines for deployment.\nDefine the production monitoringa model requires(drift detection, performance regression signals, dataquality checks).\nAct as the modeling counterpart to IT when production looks wrong, translating between model behavior and operational reality.\nHelp integrate model output into the product stack through clean, well-documented service interfaces.\nApply responsible AI practices: bias evaluation, explainability, and careful data handling.\nDocument models and explain behavior, limitations, and trade-offs to engineers, delivery teams, sales, and customers alike.\nReview peers' modeling work and help raise team standards for rigor and reproducibility.\nRequired Qualifications\nDemonstrated depth in applied machine learning and data science, with models you built that reached production and were measured there.\nStrong Python skills and fluency with the modern ML ecosystem: PyTorchor TensorFlow, scikit-learn, pandas, and NumPy.\nA solid statistics foundation: experimental design, appropriate evaluationmetrics, and the judgment to tell a real result from a lucky one.\nStrong SQL and comfort working with large relational datasets.\nExperience building data and feature pipelines that run on a schedule and survivecontact with messy source systems.\nExperience handing work to a separate operations or platform team: packaging, documenting, and specifying what a model needs to run well.\nComfort working directly with customers and delivery teams.\nTypeScript or JavaScript proficiencysufficient to integrate cleanly with our product stack.\nClear written and verbal communication.You should be able toexplain a model to a salesperson and a trade-off to an architect inthe same afternoon.\n\nBachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related technical field, or equivalent practical experience.\nPreferred Qualifications\nWorking knowledge of Kubernetes and containerized deployment.\nMLOpstooling experience such as MLflow, Kubeflow, Weights & Biases, Airflow, or Dagster.\nLLM and agentic workflow experience:retrieval-augmented generation, fine-tuning, evaluation harnesses, and vector databases.\nRecommender systems or time series forecasting at production scale.\nExposure to SAP data structures (SD, MM) or other enterprise ERP data models.\nExperience in a customer-facing implementation, delivery, or professional services capacity.\nCloud platform experience (AWS, Azure, or GCP) and SAP HANA Cloud ML libraries (PAL/APL).\nGraduate degree in machine learning, statistics, or a closely related field.\nReady to apply? Please upload your resume in English.\nDataXstreamInc., is an equal-opportunityworkplace and an affirmative-action employer. We are always committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. 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