{"id":3805986,"url":"https://alion.io/job/eaton-senior-engineer-machine-learning","title":"Senior Engineer- Machine Learning","company":{"id":6044,"name":"Eaton","domain":"eaton.com","url":"https://alion.io/company/eaton","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"A","score":86,"open_postings":305,"ghost_share":0.043,"stale_share":0.387,"repost_share":0.102,"time_to_fill_p50_days":42,"computed_at":"2026-11-07T05:47:08Z"}},"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":["Pune, India"],"countries":["IN"],"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":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Bicep","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Databricks","optional":false},{"name":"Datadog","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Grafana","optional":false},{"name":"Hadoop","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Scrum","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false}],"status":"closed","first_seen_at":"2026-10-13T07:58:22Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T12:46:22Z","board_verified":false,"closed_at":"2026-11-06T12:46:22Z","days_open":24,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":24},"description":"What you’ll do:\nEaton Corporation’s Center for Intelligent Power has an opening for a Associate Engineer- Machine Learning Operations. The ideal candidate will be responsible for developing and maintaining the infrastructure and tools required to deploy and maintain machine learning models at scale. This position requires understanding in machine learning and software engineering. The candidate will work closely with other teams to make sure the requested features by the businesses are delivered.\nAbout Eaton:\nEaton is power management company with 2018 sales of $21.6 billion. We make what matters work. Everywhere you look-from the technology and machinery that surrounds us, to the critical services and infrastructure that we depend on every day-you’ll find one thing in common. It all relies on power. That’s why Eaton is dedicated to improving people’s lives and the environment with power management technologies that are more reliable, efficient, safe and sustainable. Because this is what matters. We are confident we can deliver on this promise because of the attributes that our employees embody. We’re ethical, passionate, accountable, efficient, transparent, and we’re committed to learning. These values enable us to tackle some of the toughest challenges on the planet, never losing sight of what matters.\n- Design, develop, deploy, and maintain scalable machine learning platforms, infrastructure, and MLOps solutions in cloud environments.\n- Architect, develop, and optimize data engineering pipelines, feature engineering workflows, and CI/CD pipelines for machine learning and AI applications.\n- Build, train, validate, deploy, monitor, and continuously improve machine learning and deep learning models in production.\n- Lead the operationalization of machine learning solutions by implementing MLOps best practices, model versioning, experiment tracking, monitoring, governance, and automated retraining strategies.\n- Collaborate closely with Data Scientists, Software Engineers, Product Owners, and Architects to translate business requirements into scalable AI/ML solutions.\n- Drive adoption of software engineering best practices, reusable frameworks, templates, coding standards, and automation strategies to reduce development cycle time and improve solution reliability.\n- Troubleshoot model performance, data quality, pipeline reliability, and production issues while ensuring high availability and scalability.\n- Mentor junior engineers and contribute to technical design reviews, architecture discussions, and team knowledge-sharing initiatives.\n- Develop and maintain technical documentation, deployment guides, operational runbooks, and training materials.\n- Evaluate emerging technologies, AI/ML frameworks, cloud services, and industry best practices, and recommend their adoption where appropriate.\nQualifications:\nRequires a minimum of a Bachelor level degree in computer science or equivalent software engineering discipline.\n5 to 7 years of experience\nSkills:\nExperience with big data technologies such as Apache Spark, Kafka, Hadoop, Flink, or similar distributed systems.\nStrong experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.\nExperience developing and maintaining large-scale data engineering pipelines using Python, Spark, Databricks, Airflow, or similar technologies.\nExperience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or equivalent tools.\nStrong proficiency in Docker, Kubernetes, and container-based application deployment.\nStrong understanding of software engineering best practices including object-oriented design, design patterns, version control, testing strategies, code reviews, and secure development practices.\nExperience with model lifecycle management, experiment tracking, feature management, and production model deployment.\nDesired Expertise (in one or more of the following areas):\nProfessional certifications in Machine Learning, Cloud Technologies, Data Engineering, or MLOps.\nHands-on experience with Databricks, Snowflake, Delta Lake, Feature Stores, or Vector Databases.\nHands-on experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, MLflow, or equivalent platforms.\nExperience building Generative AI, LLM, RAG, Agentic AI, or NLP-based solutions.\nFamiliarity with Infrastructure as Code tools such as Terraform, Bicep, or CloudFormation.\nExperience with monitoring, observability, and logging platforms such as Grafana, Prometheus, ELK, Azure Monitor, or Datadog.\nExperience working in Agile/Scrum development environments.\nExperience mentoring junior engineers and leading technical initiatives across teams.","description_format":"text","description_chars":4676,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Aerospace Manufacturing","Energy Management","Power Supplies & Power Electronics","Automotive 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