Forward Deployed Engineer (Staff/ Founding)

About Katalyze AI Katalyze AI is a fast-growing AI-driven biotech platform company on a mission to make life-saving drugs accessible and affordable for everyone. Our AI Agents help pharmaceutical and biotech companies increase production efficiency, reduce costs, and minimize waste. We're a team of humble, fast-moving, and curious craftspeople working at the intersection of science and AI.

About the Role

Katalyze AI is looking for a Staff Forward Deployed Engineer to sit at the intersection of our product and our customers. You'll work directly inside enterprise pharmaceutical and manufacturing accounts, understanding their data environments, deploying and configuring the Katalyze AI platform, building custom integrations and workflows, and ensuring the product delivers real, measurable value.

This is not a traditional sales engineering or solutions consulting role. You will write production code. You'll adapt pipelines, build connectors, extend workflows, and debug complex systems in the customer environment, on their data, against their timelines. You'll also be the most important feedback loop between customers and our product and engineering teams.

At the staff level, you operate independently in ambiguous environments. You can walk into a new customer, understand their technical landscape in days, and have a working integration running within weeks.

What You'll Do

• Own the technical deployment of the Katalyze AI platform for enterprise customers from kickoff through go-live and beyond

• Build custom integrations connecting customer systems (MES, LIMS, ERP, SAP, document repositories) to the Katalyze AI platform

• Configure and extend AI-powered document processing pipelines (LLM extraction, RAG Systems, structured output validation) for customer-specific document types and workflows

• Adapt and extend workflow orchestration engines for customer-specific process requirements

• Work directly with customer IT, data engineering, and operations teams, navigating enterprise security reviews, SSO integrations, VPC configurations, and compliance requirements

• Translate customer needs and pain points into clear, actionable product feedback for the engineering team

• Build and maintain customer-specific deployment infrastructure on AWS Terraform, ECS, RDS, VPCs, CI/CD

• Set up observability and alerting for customer environments; own incident response during deployments

• Create technical documentation, runbooks, and integration guides that customers and internal teams rely on

• Represent Katalyze AI technically in customer meetings, workshops, and executive briefings

What We're Looking For

• 6+ years of software engineering experience, with demonstrated customer-facing or field deployment work

• Strong Python backend engineering: Django or FastAPI, async patterns, production-grade system design

• Hands-on LLM integration experience in production: prompt engineering, structured outputs, schema validation, cost and latency optimization

• Experience deploying and operating AWS infrastructure: ECS/Fargate, RDS, S3, CloudFront, VPC including Terraform and CI/CD pipelines

• Comfort working in customer environments with their data, their constraints, and their timelines

• Strong communicator equally comfortable presenting to a VP of IT and pair-programming with a customer data engineer

• Experience with enterprise integration patterns: REST APIs, SFTP, database connectors, SSO (SAML/Azure AD), OPC-UA or similar

• Security and compliance awareness: secrets management, IAM, SOC 2 / HIPAA controls — especially in regulated customer environments

Nice to Have

• Experience with document processing RAG systems & Knowledge Graphs

• Pharmaceutical, biotech, or manufacturing domain knowledge

• Familiarity with regulated GxP environments and compliance requirements

Tech Stack

• Backend: Python 3.12, Django 5.x, FastAPI, asyncio, BullMQ (Redis)

• Frontend: Next.js, Node.js / Fastify, TypeScript (Nx monorepo)

• AI/ML: Commercial and open-source LLMs, AWS Textract, PaddleOCR, embeddings/vector search

• Data: Snowflake (Snowpark), PostgreSQL, S3, dbt, pandas

• Infrastructure: AWS (ECS, RDS, CloudFront, Lambda), Terraform, Docker

• Observability: OpenTelemetry, Datadog / CloudWatch

• Integrations: SAP connectors, Microsoft Graph API, CyberArk SAML, Azure AD, OPC-UA