Senior ML Engineer / MLOps Engineer

EXL

Senior ML Engineer / MLOps Engineer responsible for building and deploying machine learning systems. Collaborate with data scientists for business-critical ML use cases in a hybrid work environment.

Posted 6/10/2026full-time🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AWSCloudDockerGoogle Cloud PlatformGrafanaJenkinsKubernetesPrometheusPythonPyTorchScikit-LearnSQLTensorflowTerraform

About the role

Key responsibilities & impact

Design, develop, and deploy end-to-end ML pipelines covering data ingestion, transformation, feature engineering, model training, evaluation, and production deployment.
Deploy and scale ML models on cloud platforms such as AWS (SageMaker, EKS, Lambda) or GCP (Vertex AI, GKE, Cloud Functions), ensuring robust and cost-efficient architectures.
Build and maintain CI/CD/CT pipelines using tools like GitHub Actions, Jenkins, or cloud-native services to automate model training, testing, and deployment.
Containerize applications using Docker and orchestrate using Kubernetes, while managing infrastructure through Terraform or CloudFormation.
Implement model lifecycle management practices, including model registries, versioning, and feature stores (e.g., MLflow, Feast), and establish strong observability frameworks using Prometheus and Grafana.
Develop monitoring systems to track ML performance metrics, data drift, model drift, and overall model health, ensuring timely retraining and optimization.
Collaborate closely with data scientists to productionize ML models for real-time and batch inference, enable A/B testing where applicable, and ensure smooth delivery of client-facing solutions. Provide mentorship to junior engineers and drive adoption of best practices in MLOps and software engineering.

Requirements

What you’ll need

Strong experience in ML Engineering / MLOps with demonstrated delivery of end-to-end ML solutions in production environments.
Proficiency in Python and advanced SQL, along with hands-on experience in ML frameworks such as Scikit-learn, TensorFlow, and PyTorch.
Solid understanding of machine learning algorithms, evaluation techniques, performance metrics, and validation strategies.
Hands-on expertise in cloud platforms (AWS or GCP), containerization (Docker), orchestration (Kubernetes), and CI/CD tools (Jenkins, GitHub Actions).
Familiarity with MLflow, Feast, Prometheus, Grafana, and modern model monitoring practices including data and model drift detection.

Benefits

Comp & perks

Strong problem-solving, communication, and stakeholder management skills with the ability to work independently in fast-paced environments.
Nice-to-have: Experience with real-time ML serving frameworks (KFServing, Seldon, Ray Serve), A/B testing, and experimentation platforms.
Exposure to media, subscription, or recommender systems, along with knowledge of experiment design and causal inference, will be an added advantage.

ATS Keywords

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Hard Skills & Tools

machine learning engineeringMLOpsPythonSQLScikit-learnTensorFlowPyTorchmodel lifecycle managementdata ingestionfeature engineering

Soft Skills

collaborationmentorshipbest practices adoption