Senior Machine Learning Engineer
Confirmed on the employer's own hiring board on Oct 21, 2026. First seen by Alion on Oct 14, 2026.
Role Overview
We are looking for a Senior Machine Learning Engineer with strong hands-on experience in MLOps, production ML systems, PySpark, and Databricks.
The role is focused more on deploying, operationalizing, monitoring, and maintaining ML/DL models in production than on pure research or model development.
Key Responsibilities
Design, deploy, and operationalize Machine Learning and Deep Learning models in production.
Build and maintain end-to-end MLOps workflows, covering model training, validation, deployment, monitoring, versioning, and retraining.
Develop scalable data and ML pipelines using PySpark and Databricks.
Productionize Deep Learning models and support scalable model inference.
Work with multimodal data, combining Computer Vision, textual data, metadata, and other signals to build ML solutions.
Implement model monitoring, performance tracking, drift detection, experiment tracking, and lifecycle management.
Work with cloud-based ML infrastructure and deployment platforms.
Collaborate with Data Scientists, ML Engineers, Data Engineers, and business stakeholders to deliver production-ready ML solutions.
Ensure ML systems are scalable, reliable, maintainable, and suitable for production environments.
Mandatory Skills
5+ years of experience in Machine Learning / ML Engineering.
Strong hands-on MLOps and production ML deployment experience.
PySpark - mandatory.
Databricks - mandatory.
Experience deploying Deep Learning models into production.
Strong understanding of the end-to-end MLOps lifecycle.
Experience working with Computer Vision + textual data + metadata or other multimodal data.
Experience with at least one cloud platform such as AWS, Azure, or GCP.
Strong Python programming skills.
Good to Have
Strong understanding of Deep Learning architectures.
Experience building Deep Learning models using PyTorch or TensorFlow.
Experience with Generative AI / RAG.
Understanding and practical experience with embeddings, vector databases, and semantic search.
Experience with MLflow or similar ML lifecycle/experiment tracking tools.
Experience with Kubernetes, CI/CD, model serving, or ML monitoring platforms.
What We’re Looking For
The ideal candidate is someone who can take an ML/DL solution beyond experimentation and make it production-ready - including deployment, monitoring, scaling, versioning, and ongoing lifecycle management.