Top Tech Jobs & Startup Jobs

Build and productionize agentic AI and LLM features for an enterprise SaaS platform focused on data privacy and AI governance. Responsibilities include developing LangChain and LangGraph workflows, RAG pipelines, AI asset discovery, safety guardrails, model evaluations, backend API integrations, and observability. The role requires Python, cloud AI platforms across AWS and Azure, vector databases, prompt engineering, and experience deploying ML or GenAI applications in production.

Develops interactive Power BI dashboards, reports, and data models using DAX, Power Query, and SQL. Connects to diverse data sources, transforms and validates data, publishes reports through Power BI Service, schedules refreshes, and implements row-level security. Collaborates with business stakeholders to define reporting needs and ensures responsive, high-performance report experiences.

Develop backend services and REST APIs for an enterprise SaaS platform focused on data privacy, data governance, and AI governance. Build workflow, reporting, and data-processing features using Java and Spring Boot; integrate databases and third-party APIs; write unit and integration tests; document APIs; troubleshoot defects; and collaborate with frontend, QA, and DevOps teams in an agile environment.

Lead an engineering pod developing enterprise SaaS features for data privacy, data governance, and AI governance. Own stream architecture, break down requirements, lead sprint delivery, review backend, frontend, data, and integration code, and ensure production readiness. Design microservices, APIs, and cloud integrations while managing technical risks, release quality, security, and performance. Mentor engineers and collaborate with product, architecture, UX, and QA stakeholders.

Leads delivery governance across multiple engineering pods building enterprise SaaS products for data privacy, data governance, and AI governance. Owns release planning, staffing, risk management, engineering metrics, reporting, stakeholder escalations, technical coordination, and team development. Ensures adherence to SDLC, security, compliance, CI/CD, and quality standards while partnering with technical leads and architects on capacity planning and engineering practices.

Lead frontend architecture and UX implementation for an enterprise SaaS platform using Angular and TypeScript. Build accessible, reusable, high-performing UI components, integrate REST APIs, write automated tests, optimize performance, and ensure WCAG compliance. Collaborate with UX and product teams, review code, mentor engineers, and drive usability improvements. The role also involves design systems, responsive interfaces, CI/CD, and potentially micro-frontends, React, and data visualization.

Designs, develops, deploys, and supports Snowflake-based enterprise data solutions. Responsibilities include building ETL/ELT pipelines, AWS Glue workflows, database objects, metadata-driven frameworks, and deployment automation. The role administers Snowflake, supports Aurora PostgreSQL, S3, and AWS DMS integrations, troubleshoots production data platforms, optimizes performance and costs, manages Liquibase releases, and maintains CI/CD, monitoring, documentation, and operational runbooks.

Lead technical pre-sales for Databricks engagements: run discovery, design solution architectures, scope and estimate efforts, produce proposals/SOWs, deliver demos/POCs, respond to RFIs/RFPs, maintain reusable pre-sales assets, qualify opportunities with sales, and hand off to delivery teams.

Build and maintain automation regression suites and frameworks, design and execute automated and manual tests across reporting systems and ETL/data pipelines, perform SQL-based data validation, validate data migrations and end-to-end flows, triage defects with cross-functional teams, and maintain QA documentation and test reports.

Lead design, implementation, deployment, and scaling of AI-driven applications with emphasis on RAG-based chatbots. Architect solutions using Python and Java, deploy on AWS, mentor engineers, collaborate cross-functionally, ensure testing/performance tuning, and research emerging AI technologies to drive innovation.