Senior Machine Learning Engineer - ML Platform & Operations

We’re looking for a Senior Machine Learning Engineer to help build, deploy, and operate machine learning systems in production. You’ll work across the ML lifecycle, from preparing data and deploying models to monitoring their performance and improving reliability.

The ideal candidate combines practical ML experience with strong cloud and production engineering skills. You’ll partner with data scientists, ML engineers, and software teams to make models easier to deploy, observe, and maintain at scale.

Key Responsibilities

Deploy and operate machine learning models and services in production

Support ML workflows across data preparation, model training, evaluation, deployment, and monitoring

Build and maintain reliable pipelines for model development and release

Monitor model and service performance using logs, metrics, traces, and alerts

Identify issues such as model degradation, data or concept drift, latency, and inference failures

Improve model serving systems for scalability, availability, security, and cost efficiency

Support experimentation, model versioning, evaluation, and A/B testing

Troubleshoot ML pipelines, integrations, APIs, and runtime issues

Collaborate with data science and engineering teams to improve model quality and operational reliability

Contribute to incident investigations, root cause analysis, and ongoing improvements to ML operations

Preferred Skills & Experience

5+ years of experience in machine learning, ML infrastructure, cloud engineering, platform engineering, SRE, or a related field

Hands-on experience deploying or operating machine learning models in production

Understanding of model evaluation, inference, versioning, and the ML lifecycle

Experience with at least one major cloud platform, such as Microsoft Azure or AWS

Experience with JavaScript/TypeScript is required; knowledge of Python or other programming languages is an advantage

Familiarity with cloud ML services such as Azure Machine Learning or Amazon SageMaker, or equivalent tools

Familiarity with monitoring tools such as Azure Monitor, Amazon CloudWatch, and Datadog

Understanding of logging, metrics, tracing, monitoring, and alerting

Experience with containers, APIs, and deployment pipelines

Knowledge of infrastructure-as-code tools such as Terraform or platform-specific equivalents

Familiarity with LLM applications and their evaluation or operational requirements is an advantage

DevOps experience, especially CI/CD, containers, cloud deployments, and infrastructure automation, is an advantage

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field

What We’re Looking For

Strong problem-solving, troubleshooting, and debugging skills

A practical understanding of what it takes to run ML systems reliably in production

Interest in improving model performance, deployment workflows, and ML operations

Clear communication and a collaborative approach

A focus on reliability, scalability, automation, and continuous improvement

Ability to work effectively in a fast-paced, evolving technology environment