Job Summary

Houston Skilled Consultancy is seeking a highly motivated Remote ML Ops Engineer to join our advanced data and machine learning operations team. This role is ideal for professionals who are passionate about deploying and maintaining scalable, reliable, and efficient ML systems in a cloud environment. You will play a critical role in integrating CI/CD pipelines for machine learning models, automating workflows, and bridging the gap between data science and operations to bring models from experimentation to production smoothly.

Key Responsibilities

• Design, develop, and maintain robust CI/CD pipelines for ML model deployment and monitoring.
• Collaborate with data scientists, ML engineers, and DevOps teams to streamline the model lifecycle from training to deployment.
• Manage infrastructure-as-code (IaC) for reproducible and scalable environments using tools like Terraform or CloudFormation.
• Implement version control and model registry practices for reproducibility and auditability (MLflow, DVC, etc.).
• Monitor the performance of models in production and implement automated retraining pipelines.
• Optimize ML infrastructure for cost, performance, and scalability on AWS, Azure, or GCP.
• Troubleshoot and resolve issues in data pipelines, model serving, and container orchestration frameworks (e.g., Kubernetes).
• Automate data ingestion, feature engineering pipelines, and continuous testing strategies for ML applications.

Required Skills and Qualifications

• Bachelors or Masters degree in Computer Science, Data Engineering, or a related field.
• Solid understanding of CI/CD principles and tools (e.g., Jenkins, GitHub Actions, GitLab CI).
• Hands-on experience with ML orchestration tools such as Kubeflow, MLflow, Airflow, or similar.
• Proficient in containerization and orchestration (Docker, Kubernetes, Helm).
• Experience in at least one major cloud platform (AWS, Azure, or GCP).
• Strong programming skills in Python and working knowledge of Bash/Shell scripting.
• Familiarity with data versioning and model monitoring tools.
• Understanding of MLOps best practices and ability to implement ML model observability.

Experience

• 3+ years of experience in DevOps, Data Engineering, or MLOps roles.
• Experience in deploying machine learning models in production environments at scale.
• Proven track record of working in cross-functional teams and agile environments.

Working Hours

• Flexible remote schedule aligned with project and team collaboration needs.
• Core collaboration hours: 11:00 AM – 7:00 PM IST (with flexibility as needed).

Knowledge, Skills, and Abilities

• Strong problem-solving and analytical skills.
• Ability to communicate effectively across technical and non-technical teams.
• Self-starter with a passion for automation, scalability, and operational excellence.
• Commitment to continuous learning and staying updated with the latest MLOps trends.
• Ability to work in a dynamic, fast-paced environment with minimal supervision.

Benefits

• Competitive remote salary package
• Work-from-home flexibility
• Opportunities to work with international clients and cutting-edge ML stacks
• Professional development reimbursement and access to premium learning platforms
• Friendly and inclusive team culture
• Paid time off, wellness days, and recognition programs

Why Join Houston Skilled Consultancy?

At Houston Skilled Consultancy, we empower talent to build robust systems that support data-driven innovation. You will be part of a fast-growing global team where your work will directly impact high-value machine learning products. We offer a collaborative environment, continuous learning opportunities, and the freedom to explore the latest in MLOps technology. If you are ready to transform ideas into production-grade ML systems, we want you on our team.

How to Apply

Submit your updated resume along with a brief cover letter explaining your relevant experience to us
Subject Line: Application for Remote ML Ops Engineer

Shortlisted candidates will be contacted for a virtual technical interview and case discussion.