Bachelor's degree in computer science, artificial intelligence, informatics, or a closely related field.
Master's degree is a plus.
Experience:
***At least 3 years of relevant experience as a Machine Learning Engineer.
Proven experience in deploying and maintaining production-grade machine learning models, ensuring real-time inference, scalability, and reliability.
Technical Expertise:
Proficiency in developing end-to-end scalable ML infrastructures using on-premise or cloud platforms such as AWS, GCP, or Azure.
Strong skills in creating and optimizing CI/CD pipelines for machine learning models, including automating testing and deployment processes.
Experience in developing AI pipelines for data ingestion, preprocessing, search, and retrieval.
Competence in setting up monitoring and logging solutions for tracking model performance, system health, and anomalies.
Familiarity with version control systems for tracking changes in ML models and associated code.
Understanding of security and compliance standards related to machine learning systems, including data protection and privacy regulations.
Leadership and Collaboration:
Ability to lead engineering efforts in ML/GenAI model development, LLM advancements, and optimizing deployment frameworks aligned with business strategies.
Demonstrated ability to collaborate with cross-functional teams, including data scientists, data engineers, analytics teams, and DevOps teams.
Documentation and Process Management:
Skilled in maintaining clear and comprehensive documentation of ML Ops processes, workflows, and configurations.
Preferred Qualifications:
Proficiency in containerization technologies such as Docker and Kubernetes.
Knowledge of healthcare standards, regulations, and systems, including integrating ML models with Electronic Health Records (EHR) systems.
Certifications in machine learning or related fields.