{"id":4289933,"url":"https://alion.io/job/wuerth-ml-engineer","title":"ML Engineer","company":{"id":1759262,"name":"Würth","domain":"wuerth.de","url":"https://alion.io/company/wuerth-de","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"AWS CDK","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Databricks","optional":false},{"name":"Git","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"MS SQL","optional":false},{"name":"MySQL","optional":false},{"name":"Python","optional":false},{"name":"SAP HANA","optional":false},{"name":"Scala","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon ECS","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"API Gateway","optional":true},{"name":"AWS Lambda","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"Azure","optional":true},{"name":"Delta Lake","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"IAM","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true},{"name":"Platform Engineering","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-11-13T22:58:06Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T22:58:06Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"ML Engineer - AI & Data Platform\nAs an ML Engineer, you design, build, and operate production-grade machine-learning services on our AI & Data platform. You bridge robust software engineering with practical ML expertise: turning models, data products, and AI use cases into secure, scalable, observable services that deliver lasting business value.\nThis role is intentionally software-engineering-led. We are looking for engineers who enjoy building reliable systems and have the ML knowledge to productionize them.\nYou will work primarily with Databricks and AWS, partnering closely with data scientists, data engineers, cloud engineers, and product teams.\nWhat you will do\nBuild, deploy, and operate ML-powered services, pipelines, and APIs using Python, SQL, Databricks, AWS, Linux, and infrastructure-as-code.\nTranslate ML prototypes and business requirements into maintainable, production-ready software with clear interfaces, automated testing, and operational documentation.\nOwn services throughout their lifecycle-from technical design and implementation through deployment, monitoring, incident resolution, and continuous improvement.\nEngineer reliable data and feature flows for training, evaluation, inference, and model retraining.\nEstablish and evolve MLOps practices, including model versioning, experiment tracking, CI/CD, automated validation, release processes, observability, and rollback strategies.\nBuild, maintain, and continuously improve reusable engineering standards, reference architectures, templates, and starter kits for ML and data projects-for example Databricks Asset Bundles (DABs) and AWS CDK templates.\nTurn platform standards into practical, well-documented building blocks that project teams can adopt with minimal friction.\nAdvise, enable, and coach cross-functional project teams in designing efficient, scalable Big Data and ML solutions; explain architectural choices, guardrails, and delivery standards clearly and pragmatically.\nReview project implementations, identify gaps against platform standards, and support teams in resolving them without losing sight of delivery needs.\nCollaborate with platform, governance, infrastructure, data provisioning, scheduling, CI/CD, security, and data engineering teams to deliver integrated solutions.\nImprove platform standards, reusable components, developer experience, service reliability, and cost efficiency.\nDiagnose and resolve complex issues across applications, data pipelines, cloud services, and operating environments.\nKeep current with relevant developments in cloud engineering, MLOps, Databricks, and applied machine learning-and turn the useful ones into practical improvements.\nWhat you bring\nSoftware engineering\n5+ years of professional software engineering experience, building and maintaining production systems.\nStrong proficiency in Python and experience with at least one additional programming language, such as Java, Scala, C++, or Go.\nA track record of designing clean, modular, maintainable, and well-tested software.\nExperience with code reviews, unit and integration testing, debugging, API design, dependency management, and secure software development practices.\nStrong knowledge of Git and modern collaborative development workflows.\nExperience working in agile, cross-functional product or platform teams.\nML engineering and data platforms\n2+ years of hands-on ML Engineering, MLOps, or closely related production ML experience.\nExperience taking ML workloads from experimentation into production, including training, evaluation, deployment, monitoring, and maintenance.\nPractical understanding of the ML lifecycle, common model-performance concerns, data quality, reproducibility, and model/version management.\nStrong SQL skills and experience working with relational databases, such as SAP HANA, Microsoft SQL Server, or MySQL.\nExperience with Big Data processing and/or a modern data platform; practical experience with Databricks is highly valued.\nCloud and infrastructure\n2+ years of hands-on AWS experience across several services, with the ability to make sound architecture and operational decisions.\nExperience using infrastructure-as-code, such as AWS CDK, Terraform, or CloudFormation.\nStrong Linux and operating system fundamentals, including networking, processes, permissions, logging, and troubleshooting.\nExperience with CI/CD pipelines and automated deployments.\nA pragmatic mindset for security, reliability, performance, observability, and cloud-cost management.\nCollaboration\nYou approach ambiguous problems methodically, balance speed with quality, and take ownership of outcomes.\nYou communicate technical decisions clearly to both engineering and non-engineering stakeholders.\nYou enjoy collaborating, sharing knowledge, and raising engineering standards across teams.\nYou have strong written and spoken English skills.\nNice to have\nDatabricks, AWS, Kubernetes, or Terraform certifications.\nExperience with Databricks capabilities such as Delta Lake, Workflows, MLflow, Unity Catalog, or model serving.\nPractical knowledge of AWS services such as Lambda, SageMaker, API Gateway, ECR, ECS/EKS, Step Functions, S3, IAM, CloudWatch, or SNS.\nExperience with containerization and orchestration, for example Docker and Kubernetes.\nFamiliarity with Azure or Google Cloud Platform.\nExperience operating event-driven, distributed, or high-throughput data systems.\nExposure to LLM applications, vector search, retrieval-augmented generation, or AI platform engineering.\nWhat success looks like\nWithin your first months, you will have contributed to reliable, well-documented ML services that we can confidently run in production. You will help move ML use cases beyond prototypes by improving deployment quality, operational visibility, reuse, and cost efficiency-while making the platform easier for others to build on.\nWhy this role matters\nMachine-learning initiatives create value only when they operate reliably in the real world. As an ML Engineer, you make that possible: you bring engineering discipline to ML workloads and help create a platform where data and AI teams can deliver secure, scalable, and sustainable products.","description_format":"text","description_chars":6165,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Mechanical Components & Fasteners","Tools & DIY Supplies"],"lifecycle":[{"event":"open","at":"2026-11-13T22:58:06Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":20,"reasons":["conf:0","win:early"],"computed_at":"2026-11-13T23:45:01Z"},"pay":null,"html_url":"https://alion.io/job/wuerth-ml-engineer","json_url":"https://alion.io/job/wuerth-ml-engineer.json","meta":{"generated_at":"2026-11-13T23:45:01Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":47384,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-14T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1759262},"rest":"https://alion.io/mcp/rest/get_company?id=1759262"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fwuerth-ml-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"free","price":{"credits":0,"usd":0},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fwuerth-ml-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fwuerth-ml-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/wuerth-ml-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fwuerth-ml-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4289933}},{"tool":"search_jobs","arguments":{"company":"wuerth.de","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4289933},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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