ML Ops Engineer

We are seeking a skilled Machine Learning Engineer/AI Specialist to join our dynamic team. The ideal candidate will have extensive AWS Sage Maker, strong Python programming skills, a solid background in data science, and a deep understanding of MLOps practices.

• Design, develop, and deploy machine learning models using AWS Sage Maker platform.
• Build and maintain ML pipelines for training, validation, and deployment of models.
• Implement MLOps best practices including CI/CD for machine learning workflows.
• Collaborate with data scientists to produce research models.
• Monitor model performance and implement automated retraining processes.
• Optimize model inference performance and cost efficiency.
• Develop and maintain model versioning and experiment tracking systems.
• Ensure data quality and implement data validation frameworks.
• Create comprehensive documentation and technical specifications.
• Participate in code reviews and maintain high coding standards.
• Debug Terraform and Concourse errors.
• Proactively update pipelines based on changes made by other organizations.
• Migrate repository to Git Hub and update pipelines accordingly.

• Bachelor's degree in Computer Science, Data Science, Engineering, or related field; or 8 years of equivalent work experience.
• 3+ years of experience in machine learning engineering or related roles.
• Proficiency in Python programming with experience in ML libraries (pandas, numpy, etc.).
• Familiarity with Infrastructure as Code (Terraform, Cloud Formation).
• Hands‑on experience with AWS Sage Maker for model training, tuning, and deployment.
• Strong background in data science methodologies and statistical analysis.
• Deep understanding of MLOps practices and tools (Docker, Kubernetes, CI/CD pipelines).
• Experience with version control systems (Git Hub Actions) and collaborative development.
• Knowledge of cloud platforms, preferably AWS (S3, EC2, Lambda, etc.).

• Master's degree in a relevant field.
• Knowledge of containerization and orchestration technologies.
• Experience with monitoring and observability tools (Cloud Watch, Prometheus, etc.).
• Experience with big data technologies (EMR, Spark, Hadoop, etc.).
• Understanding of software engineering best practices and design patterns.
• Good working experience in ETL (SSIS or Sqoop/Spark).
• Experience with EMR
• Expert SQL knowledge (All types of Joins, CTE’s, Indexes, Stored Procedures, SQL performance).
• Knowledge in building basic machine learning models (Classification & Regression).
• Knowledge in Docker/MLOps and its orchestrations.
• Strong analytical and problem‑solving abilities.
• Excellent communication and collaboration skills.
• Ability to work in fast‑paced, agile environments.
• Detail‑oriented with a focus on code quality and documentation.
• Continuous learning mindset and adaptability to new technologies.
• Experience working cross‑functionally with data scientists, engineers, and product teams.