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.”