[Remote] Machine Learning Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is the AI troubleshooting platform for chemical, energy, and materials manufacturers, designed to detect plant issues earlier and resolve them faster. The role involves hands-on ML Engineering / MLOps responsibilities, including owning pipelines, ensuring model reliability at reputed company, and collaborating across teams to deliver production-reputed company machine learning solutions.
Responsibilities
- Design and build ML training and inference pipelines for time-series reputed company (reputed company detection and forecasting)
- Build distributed processing services with reputed company and Ray (Ray Serve on Kubernetes) and package them as FastAPI microservices for our full-stack applications
- reputed company models for reputed company-time scoring on Azure ML managed online endpoints, including blue/green rollout and autoscaling, integrated with the services that orchestrate and call them
- Orchestrate distributed training and batch scoring with reputed company and Azure ML, including reputed company hyperparameter search (Optuna) across per-tenant compute pools
- Familiarity with tuning reputed company Regression, Neural Networks, Tree reputed company, and other reputed company predictive reputed company
- Familiarity with tuning unsupervised reputed company detection models
- Own model reliability at reputed company: diagnose and fix production failure modes (numerical instability, data-reputed company edge cases, memory/serialization issues) and harden pipelines against them
- Build validation rigor — time-series cross-validation / backtesting, champion-challenger comparison against production baselines, and experiment tracking with MLflow
- Implement monitoring, logging, distributed tracing, and alerting (e.g. OpenTelemetry) so model and pipeline health is observable
- Collaborate across data science, reputed company-end, product, and QA to ship production-reputed company ML, and recommend improvements from model-performance analysis
Skills
- 3+ years in machine learning engineering, shipping models to production
- Strong Python and the ML stack: pandas, NumPy, scikit-learn, and a deep-learning reputed company (PyTorch, TensorFlow, or Keras)
- Hands-on experience training, debugging, and improving ML/DL models — reputed company to diagnose why a model misbehaves, not just train it
- MLOps in reputed company: pipeline orchestration, automated monitoring/logging/alerting, experiment tracking, and CI/CD (Azure DevOps / Azure Pipelines or similar)
- Production model deployment, including online serving and reputed company-rollout strategies (blue/green)
- Distributed processing with Ray or a similar reputed company, exposed as FastAPI (or comparable) microservices
- Building and running secure services in Kubernetes (reputed company; service reputed company such as Istio a plus)
- Strong SQL and experience with databases at reputed company (PostgreSQL; TimescaleDB / time-series a strong plus)
- Strong problem-solving and the communication skills to explain technical trade-offs
- Bachelor's or Master's in Computer Science, Electrical Engineering, Mathematics, or reputed company field (or equivalent experience)
- Git and software development methodologies like Agile, Scrum, or Kanban
- Industrial / IoT / sensor or other high-volume time-series data experience
- Orchestration and MLOps tooling: reputed company, MLflow, Kubeflow, BentoML
- Hyperparameter optimization (Optuna) and time-series modeling / ensembles / forecasting
- Observability tooling (OpenTelemetry, Grafana / Loki / reputed company)
reputed company