Top Tech Jobs & Startup Jobs

Designs, deploys, and governs AI platform capabilities across Azure and Snowflake. Responsibilities include operating MCP servers, integrating AI clients and agents with enterprise systems, implementing secure data access and governance controls, establishing APIs and connectivity patterns, and supporting compliance audits. The role also collaborates with data, security, infrastructure, and product teams on data quality, lineage, observability, identity management, network security, and optional Microsoft 365 integrations.

Develop and maintain advanced iOS applications and scalable Node.js APIs for healthcare environments. Integrate REST, GraphQL, third-party services, and event-driven systems while supporting offline synchronization with NoSQL databases. Manage containerized deployments and cloud infrastructure using Docker, Kubernetes, GCP, and Anthos. Ensure HIPAA-compliant data handling, secure authentication, observability, performance, testing, and maintainability. Collaborate cross-functionally, contribute to architecture decisions, troubleshoot applications, and mentor junior developers.

The Data Migration Engineer will migrate and rebuild data pipelines from SQL Server to Snowflake using Matillion. Responsibilities include gathering business requirements, assessing data feeds, identifying gaps, replicating stored procedures and views, validating data accuracy, reconciling records, supporting migration testing, documenting decisions, collaborating with engineering teams, and transferring knowledge to internal users.

Design, develop, and deploy AI-powered enterprise solutions using LLMs, Gemini, GitHub Copilot, Glean, RAG, and knowledge graphs. Integrate generative AI into workflows, optimize models, benchmark performance, and build scalable applications with engineering and data science teams. The role also requires attention to AI security, compliance, ethical usage, healthcare use cases, data privacy, and production deployment across cloud platforms.

Own the complete data science lifecycle for a flagship predictive modeling project. Responsibilities include analyzing and engineering multi-source data, building production-grade pipelines, developing and deploying supervised, unsupervised, reinforcement, deep learning, NLP, and forecasting models, monitoring performance, documenting work, communicating insights, and mentoring junior team members.

Design, develop, and optimize scalable batch and real-time data pipelines on GCP. Implement Medallion Architecture, build Python and PySpark transformations, optimize BigQuery and SQL workloads, deploy Dataflow pipelines, and orchestrate workflows with Cloud Composer and Airflow. Manage GCS storage, CI/CD, version control, IAM, data governance, security, reliability, scalability, and cost efficiency across enterprise data platforms.

Define and govern enterprise data architecture, integration frameworks, data models, governance standards, and scalable GCP data platforms. Lead architecture decisions covering batch and real-time processing, Data Mesh and Lakehouse patterns, ETL/ELT, APIs, streaming, security, performance, observability, privacy, and compliance. Partner with technical and business stakeholders, guide data initiatives, establish platform standards, and mentor engineering teams on architecture and integration best practices.

Own requirements, roadmaps, workflows, and data specifications for AI-powered clinical summarization and decision-support products. Collaborate with data scientists, engineers, and delivery teams to translate business needs into AI features. Lead stakeholder discovery, ensure clinical accuracy and data integrity, and oversee evaluation and validation of AI outputs in healthcare and clinical environments.

Build and deploy full-stack, backend-heavy applications and scalable Java services on GCP. Develop AI-powered solutions using Vertex AI and agentic AI frameworks, owning development, testing, deployment, and production support. Collaborate with clients and cross-functional stakeholders in ambiguous, forward-deployed environments, rapidly prototyping solutions, troubleshooting issues, and iterating based on user feedback.