Machine Learning Engineer

Role: ML Ops Engineer/ Architect

Location: Hyderabad (Remote)

Industry: Healthcare/ Payer (must)

Qualifications

• Cross-Functional Collaboration and Stakeholder Management: Partner with data science, product management, engineering, and business teams to understand their requirements and ensure the MLOps platform effectively supports their needs
• Cross-Disciplinary Knowledge: Apply knowledge from related disciplines, such as data science and health/biology sciences, to design holistic MLOps solutions that meet the unique needs of the organization
• 8+ years of experience in ML Ops, Data Engineering, or related role required
• Service and Quality Excellence: Ability to demonstrate an uncompromising commitment to delivering exceptional care to create an unmatched value proposition for our patients
• Honor our Mission and Values: Ability to build trust and act with authenticity to cultivate a culture of integrity, inclusion, and mutual respect
• Attain and Leverage Strategic Relationships: Ability to develop and strengthen collaborative relationships with both internal and external stakeholders

Responsibilities

• This role combines deep cloud architecture expertise with advanced AI/ML knowledge to develop solutions that streamline workflows, enable seamless collaboration, and drive innovation
• As a key contributor to the organization's AI/ML strategy, you will partner with cross-functional teams, including data engineers/scientists, product managers, and cloud engineers, to align platform development with business objectives
• Your work will directly support the deployment of Responsible AI solutions that prioritize transparency, fairness, and ethical practices
• Platform Development: Lead the enhancement of the ML Ops platform to improve the developer experience for data and ML engineers
• Optimize workflows by integrating state-of-the-art tools and technologies, ensuring scalability and efficiency
• Cloud Infrastructure Design and Management: Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform
• Optimize for scalability, security, cost-effectiveness, and high availability
• Effectively communicate technical concepts and strategies to both technical and non-technical audiences
• AI/ML Reliability and Observability: Collaborate with the AI/ML reliability engineering team to design and implement components that ensure the platform's operational reliability, observability, and fault tolerance
• DevOps for Machine Learning Workloads: Build and maintain robust DevOps pipelines tailored for ML workflows, enabling automated model training, testing, deployment, and monitoring
• Tool Development and System Reliability: Design and manage tools to enhance platform reliability, including dashboards, logging systems, and alerting frameworks, to ensure seamless operations
• Practices and adheres to the Code of Conduct philosophy and Mission and Value Statement.