Top Tech Jobs & Startup Jobs

Manage strategic enterprise accounts, develop quarterly account plans, lead executive business reviews, and identify retention, renewal, adoption, and expansion opportunities. Build relationships across complex stakeholder groups, support new business pursuits, coordinate with Sales and Customer Success, maintain forecasts and account records, and use AI tools for account research and opportunity identification. The role requires enterprise account management experience, commercial acumen, strong communication skills, and exposure to life sciences quality and regulatory domains.

Develops customer-facing reports and dashboards using regulatory intelligence data. Translates customer requests into scoped queries, extracts and validates data, builds visualizations and executive reports, performs QA, and manages delivery. The role requires interpreting FDA enforcement actions, Form 483 observations, warning letters, and inspection records while ensuring accurate, polished outputs. It also involves using BI tools, SQL, and AI-assisted workflows to support data exploration and reporting.

Design, build, optimize, and maintain scalable data pipelines, APIs, and microservices for high-volume regulatory datasets. Ensure data quality through automated testing, monitoring, and validation; contribute to platform architecture; integrate with NLP/ML services and ELK; support production deployments and incidents; and collaborate with Engineering and Product teams. Mentor engineers, participate in code reviews, and provide technical guidance.

Leads and develops scalable data pipelines, APIs, microservices, and ETL/ELT systems for regulatory and health agency datasets. Ensures data quality through automated testing, monitoring, and validation; integrates pipelines with NLP, machine learning, and search services; and supports production incident resolution. Mentors junior and mid-level engineers, establishes technical standards, conducts code reviews, and translates architecture and product requirements into actionable engineering work.

Build and deploy production-grade AI applications using LLMs, generative AI, conversational and agentic frameworks. Develop Python and FastAPI backend services, scalable microservices, hybrid search using vector and graph databases, and integrations with data pipelines and platform APIs. Maintain AI systems in production, including testing, monitoring, troubleshooting, and performance optimization while collaborating with product and engineering teams.

Develop and maintain scalable AI and ML architectures for a life sciences regulatory intelligence platform. Lead technical decisions, integrate LLM services and microservices, build conversational and agentic systems, manage data stores and databases, support production delivery, and collaborate in an Agile Scrum environment. The role requires hands-on Python development, generative AI deployment, cloud architecture, vector and graph databases, hybrid search, and backend API development.