Machine Learning Engineer

Responsibilities:

• Deploying and maintaining production-grade machine learning models, ensuring real-time inference, scalability, and reliability.
• Developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as AWS, Google Cloud Platform, or Azure.
• Leading engineering efforts in creating and implementing methods and workflows for ML/GenAI model engineering, LLM advancements, and optimizing deployment frameworks.
• Developing AI pipelines for various data processing needs, ensuring solutions meet all technical and business requirements.
• Collaborating with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines.
• Implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes.
• Setting up monitoring and logging solutions to track model performance, system health, and anomalies.
• Implementing version control systems for machine learning models and associated code.
• Ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations.
• Maintaining clear and comprehensive documentation of ML Ops processes and configurations.

Qualifications:

• Bachelor's degree in computer science, artificial intelligence, informatics or a closely related field. Master's degree in computer science, engineering or a closely related field.
• Revant Machine Learning Engineer Experience.
• Experience with AI and machine learning platforms (e.g., AWS, Azure or Google Cloud Platform).
• Proficiency in containerization technologies (e.g., Docker) or container orchestration platforms (e.g., Kubernetes).
• Experience with CI/CD tools (e.g., GitHub Actions).
• Proficiency in programming languages and frameworks (e.g., Python, R, SQL).
• Deep understanding of coding, architecture, and deployment processes.
• Strong understanding of critical performance metrics.
• Extensive experience in predictive modelling, LLMs, and NLP.
• Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems.
• Experience in managing end-to-end ML lifecycle and automation with Terraform is a must.
• Ability to effectively articulate the advantages and applications of the RAG framework with LLMs.