Overview
Skills
Job Details
Position: Cloud Solution Architect (Life Sciences Domain)
Location: Boston preferred or EST Time zone (Hybrid)
Skills: Strong data bricks, AWS, Experience in LS (R&D nice to have), Experience in developing solution using AI/Gen AI.
Job Summary:
We are seeking an experienced Cloud & AI Solution Architect with deep expertise in AWS, Databricks, and AI/GenAI-based solution design. The ideal candidate will play a key role in architecting and implementing scalable cloud and data solutions within the Life Sciences domain (R&D experience preferred). This role involves hands-on architecture, solution design, and technical leadership for cloud-native and AI-driven initiatives.
Key Responsibilities:
• Design and architect end-to-end data and AI solutions leveraging AWS and Databricks platforms.
• Define cloud architecture blueprints, data pipelines, and AI/GenAI workflows aligned with business goals.
• Partner with data scientists, engineers, and business teams to deliver scalable AI-enabled solutions.
• Architect and implement data lakehouse solutions using Databricks (Spark, Delta Lake, Unity Catalog).
• Develop and operationalize AI/GenAI models, intelligent agents, and automation frameworks.
• Ensure solutions follow cloud security, compliance, and governance best practices—especially in Life Sciences data environments.
• Lead technical discussions, perform architecture reviews, and guide implementation teams.
• Evaluate emerging technologies and propose strategies to enhance scalability, cost efficiency, and innovation.
Required Skills & Experience:
• 8+ years of IT experience with 3+ years in cloud architecture and data engineering.
• Strong hands-on experience in AWS Cloud Services — S3, Lambda, Glue, Redshift, SageMaker, API Gateway, ECS/EKS, CloudFormation/CDK.
• Proven expertise in Databricks (Spark, Delta Lake, Workflows, MLflow).
• Strong programming experience in Python for data processing and AI model integration.
• Experience in designing and deploying AI/GenAI-based solutions, agents, or automation workflows.
• Solid understanding of data lakehouse, ETL/ELT patterns, and MLOps pipelines.
• Exposure to Life Sciences domain (R&D experience preferred) with understanding of data standards and compliance.
• Ability to translate business requirements into technical architecture.
• Excellent communication and presentation skills with experience in stakeholder management.