MLOps, DevOps & Platform Engineer

Key Responsibilities:

• Design, develop, and implement MLOps pipelines for the continuous integration, deployment, and monitoring of machine learning models.
• Design cloud-based automation workflows using Infrastructure-as-Code tools (e.g., Terraform, CloudFormation) to support scalable healthcare data and analytics environments
• Build modular frameworks for provisioning, deploying, and configuring infrastructure across multi-cloud platforms tailored to healthcare compliance and performance needs
• Develop service catalog components compatible with platforms like Backstage to streamline clinical and operational automation
• Integrate Generative AI models to enhance service catalog capabilities, including automated code generation, validation, and clinical documentation support
• Architect CI/CD pipelines for automated build, test, and deployment of healthcare analytics and AI solutions
• Maintain deployment automation scripts using Python or Bash to support secure and efficient healthcare system operations
• Implement Generative AI models (e.g., RAG, agent-based workflows) for AIOps use cases such as anomaly detection in patient monitoring systems and root cause analysis in clinical workflows
• Leverage AI/ML tools like LangChain, Bedrock, Vertex AI, or Azure AI to build advanced Generative AI solutions for healthcare applications
• Develop vector databases and document sources using Amazon Kendra, OpenSearch, or custom solutions to support intelligent search across clinical knowledge bases
• Engineer real-time data pipelines to stream operational insights from healthcare systems, enabling AI-driven automation and decision support
• Build MLOps pipelines to deploy and monitor Generative AI models, ensuring performance, compliance, and mitigation of model drift in clinical environments
• Select and integrate appropriate LLMs for specific healthcare AIOps use cases, such as patient triage, clinical summarization, or risk prediction
• Collaborate with cross-functional teams including clinical informatics, data engineering, compliance, and product teams to refine AI-driven healthcare automation processes
• Continuously research emerging technologies to enhance scalability, security, and efficiency of healthcare AI infrastructure
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Key Skills:

• Expert-level Python for production systems.
• Proficiency with Infrastructure-as-Code tools like Terraform or CloudFormation for deploying secure, compliant healthcare systems
• Advanced expertise in Python, with hands-on experience in Generative AI frameworks such as RAG and agent-based workflows applied to clinical and operational use cases
• Knowledge of cloud-based AI services including AWS Bedrock, Google Vertex AI, or Azure AI, tailored for healthcare data privacy and scalability
• Familiarity with vector databases like Amazon Kendra, OpenSearch, or custom solutions for intelligent search across clinical documentation and medical literature
• Competency in data engineering tasks such as feature engineering, data labeling, and real-time streaming of healthcare operational data
• Proven experience in building and maintaining MLOps pipelines for deploying and monitoring AI/ML models in production healthcare environments
• Background in Flow Engineering tools such as LangGraph or platform-specific orchestration tools for managing healthcare automation workflows
• Understanding of AIOps processes to refine cloud-based automation for clinical systems, patient monitoring, and operational analytics.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field.
• 8+ years of proven experience in leading AI/ML research projects and teams.
• Strong programming skills in Python, R, and SQL.
• Excellent problem-solving skills and the ability to work independently and collaboratively.
• Strong communication skills and the ability to present complex technical concepts to non-technical stakeholders.
• Familiarity with AIML governance, ethics, and responsible AI practices