Aya Healthcare is seeking a Data and AI/ML Architect to design and implement scalable data platforms and intelligent systems that power analytics, automation, and decision-making across the organization, bridging data engineering, cloud architecture, and machine learning.
Requirements
- Deep experience with cloud data platforms (Azure Synapse, AWS Redshift, GCP BigQuery).
- Proficiency in Python, SQL, and modern data frameworks (Spark, Databricks, Snowflake, etc.).
- Solid grounding in MLOps, data pipelines, API design, and data security.
- Experience with GenAI/LLM-based applications (OpenAI, Anthropic, Azure OpenAI, etc.).
- Familiarity with vector databases, knowledge graphs, or AI orchestration frameworks.
- Certifications in cloud or data architecture (e.g., Azure Solutions Architect, AWS Data Analytics Specialty).
Responsibilities
- Design end-to-end data and AI architectures across cloud and hybrid environments (e.g., Azure, AWS, GCP).
- Define data and model lifecycle management frameworks that support ingestion, transformation, storage, and consumption at scale.
- Develop robust, modular data pipelines using tools such as Databricks, Spark, Airflow, or Synapse.
- Implement Agile-friendly CI/CD frameworks for data and ML workloads to support incremental delivery and continuous improvement.
- Architect scalable solutions for machine learning, generative AI, and predictive analytics, from experimentation to production deployment.
- Integrate MLOps and Agile DevOps practices for model lifecycle management, leveraging tools like MLflow, Kubeflow, Vertex AI, or Azure ML.
- Lead the adoption of data lakehouse and semantic layer architectures to unify structured and unstructured data sources.
Other
- Partner with stakeholders in Agile planning sessions, sprint reviews, and backlog prioritization to translate business goals into technical architectures and roadmaps.
- Champion Agile and DevOps methodologies, promoting iterative development, continuous learning, and feedback-driven improvement.
- Mentor engineers, data scientists, and analysts in modern AI and data architecture practices.
- Collaborate closely with product owners, Scrum Masters, and stakeholders to align AI initiatives with business value creation.
- Stay ahead of emerging trends in data platforms, AI frameworks, and responsible AI governance.
