MindWise Technologies | AI Architect
3 weeks ago
Company Description
MindWise is dedicated to revolutionizing the US healthcare industry through cutting-edge IT solutions. They provide tailored technology services to empower healthcare organizations for better patient outcomes, operational efficiency, and innovation. Services include custom software development, healthcare IT consulting, and healthcare analytics, all designed to adapt and scale with evolving healthcare demands.
Role and Responsibilities
- This is a full-time remote role for an AI Architect at MindWise Technologies.
- You will be responsible to design, build, deploy & continuously optimize cloud native Machine Learning system.
- You would work closely with other Data & ML Engineers to design and develop data/ML pipelines for continuous training of various AI/ML models.
- As part of architecture development, you will work together with Data Scientists, data engineers and model development team to enable the ML Model end-to-end lifecycle i.e., from Commissioning training datasets, deploying it in PROD environment & implement model drift by following best practices in model monitoring.
- As part of the design and architecting principles, will ensure seamless deployment and efficient re-use in multi project variant scenarios with focus on ensuring cost-effective deployments.
- As part of the job responsibility, will also enable the downstream users and core development team to define common touch point for integration.
- As part of the design principles, you will ensure automation at all steps of validation .
- The AI Architect will be responsible for architectural design, software development, integration, and project management. Day-to-day tasks include designing AI solutions, overseeing development processes, integrating AI technologies with existing systems, and managing project timelines and deliverables.
- Hands-on experience with scaling using containerization in a cloud environment (AWS).
Mandatory Skills
- Strong programming skills in Python.
- Hands-on experience with cloud native MLOps frameworks and services (AWS).
- Good SQL knowledge.
- Experience with CI/CD practices, DevOps, and MLOps principles.
- Experience with DevOps, data pipelines, AWS ECR registry, AWS ECS, AWS EC2, AWS EKS, deployment methods viz. Docker, K8s, etc.
- Experience in Model monitoring
- Good knowledge of ML model development and deployment life cycle.
Preferred Skills
- Experience with functional and system integration testing.
- Experience working with Agile methodologies and frameworks.
- Functional understanding of core data engineering concepts – data management, data lakes, data analysis and data lifecycle.
- Architecture and Architectural Design skills
- Software Development and Integration expertise
- Project Management experience
- Strong analytical and problem-solving abilities
- Knowledge of AI and machine learning technologies
- Excellent communication and teamwork skills
Qualifications
- Experience: 12-15 years of experience, including DevOps with 6+ years’ experience in Machine Learning Engineering & 3+ years’ experience in managing at least 3 members.
- AWS Solution Architect Certification is a must.
- Bachelor's or Master's degree in Computer Science, Information Technology, or related field
- Previous experience in healthcare IT is a plus
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