Overview: We are seeking a motivated and experienced MLOps Engineer to join our team and play a key role in operationalizing machine learning workflows. The ideal candidate will have a solid understanding of ML fundamentals, hands-on experience with MLOps tools and practices, and the ability to build, scale, and automate end-to-end ML pipelines in cloud environments.
Key Responsibilities:
ML Pipeline Development:
Design, implement, and maintain scalable ML pipelines from model development to deployment and monitoring using tools like Kubeflow, Airflow, or SageMaker.
Automate the ML lifecycle including data preprocessing, model training, evaluation, versioning, and deployment.
Collaboration and Deployment:
Collaborate with data scientists to operationalize ML models built using scikit-learn, TensorFlow, or PyTorch.
Implement and manage CI/CD pipelines for ML systems using GitHub Actions, GitLab CI, or Jenkins.
Containerize ML workloads using Docker and orchestrate them with Kubernetes for scalable deployment.
Infrastructure Management:
Develop and maintain infrastructure-as-code scripts and configurations for reproducible environments.
Monitor model performance and infrastructure using appropriate logging and alerting tools.
Ensure security, scalability, and reliability of ML deployments on cloud platforms (GCP, AWS, Azure).
Code Quality:
Write clean, maintainable code and automation scripts in Python and Bash/Shell.
Technical Skills:
Category-wise:
Programming and Scripting:
Strong programming skills in Python and experience with Bash/Shell scripting.
ML Frameworks:
Solid understanding of the ML model lifecycle and experience with ML frameworks like scikit-learn, TensorFlow, or PyTorch.
MLOps Tools:
Hands-on experience with MLOps tools such as Kubeflow, Airflow, or Amazon SageMaker.
Containerization and Orchestration:
Proficiency in containerization and orchestration using Docker and Kubernetes.
CI/CD Pipelines:
Experience building and maintaining CI/CD pipelines for ML workflows.
Cloud Platforms:
Familiarity with one or more cloud platforms (GCP, AWS, Azure) and their ML services.
Model Monitoring:
Understanding of model evaluation metrics and how to monitor model performance in production.
Day-to-Day Activities:
Designing and maintaining scalable ML pipelines.
Automating the ML lifecycle including data preprocessing, model training, evaluation, versioning, and deployment.
Collaborating with data scientists to operationalize ML models.
Implementing and managing CI/CD pipelines for ML systems.
Containerizing ML workloads and orchestrating them with Kubernetes.
Developing and maintaining infrastructure-as-code scripts for reproducible environments.
Monitoring model performance and infrastructure.
Writing clean, maintainable code and automation scripts.
Qualifications:
Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
Experience:
4+ years of experience in MLOps or related roles.
Strong programming skills in Python and experience with Bash/Shell scripting.
Solid understanding of ML frameworks like scikit-learn, TensorFlow, or PyTorch.
Hands-on experience with MLOps tools such as Kubeflow, Airflow, or Amazon SageMaker.
Proficiency in containerization and orchestration using Docker and Kubernetes.
Experience building and maintaining CI/CD pipelines.
Familiarity with cloud platforms (GCP, AWS, Azure).
Soft Skills:
Communication:
Excellent written and verbal communication skills.
Problem-Solving:
Strong problem-solving skills and ability to work in cross-functional teams.
Collaboration:
Ability to work effectively in a team environment.
Adaptability:
Ability to work under pressure and meet tight deadlines.
SYNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
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