Senior Forward Deployed Engineer, AI
Leads end-to-end deployment of AI-native solutions into complex enterprise healthcare environments, combining production software engineering, cloud infrastructure, data integrations, security judgment, and customer-facing technical leadership.
About the job
Responsibilities
- Design, build, and deploy AI-native solutions inside customer environments, including MCP servers, agentic workflows, and custom integrations.
- Own infrastructure for customer deployments, including Terraform-managed AWS environments, VPC networking, IAM, security controls, and data isolation.
- Work with large-scale data across Databricks, Delta Lake, Snowflake, and S3; debug pipeline failures, optimize performance, and build production-grade integrations.
- Lead technical engagement with customer stakeholders by scoping work, communicating progress, explaining tradeoffs, surfacing risks, and building trust.
- Turn field learnings into reusable deployment patterns, templates, engineering standards, and roadmap input.
- Lead high-complexity customer deployments from solution architecture through production delivery.
- Build and ship production-grade agentic workflows and MCP servers.
- Lead architecture reviews, navigate compliance constraints, and contribute to architecture governance.
- Partner with Sales, Product, Data, and Core Engineering to close gaps and shape the roadmap.
Requirements
- 7+ years of software engineering experience building and owning production systems.
- Hands-on experience building LLM-powered applications, agentic workflows, or AI tools beyond prototypes.
- Strong AWS and Terraform experience, including VPC networking, IAM, security controls, and customer or single-tenant environments.
- Strong Python skills; experience with APIs, asynchronous service patterns, and dependable production software.
- Experience with large-scale data systems such as Databricks, Delta Lake, Snowflake, S3, or similar platforms.
- Experience designing for data isolation, customer security requirements, and regulated environments such as SOC 2, BAA, or HIPAA.
- Experience building integration surfaces for AI systems, including MCP servers or equivalent patterns.
- Ability to lead technical conversations with customer engineers and senior stakeholders.
- Ability to work across infrastructure, application, data, and AI layers and operate effectively amid ambiguity.
Nice to Have
- Experience with LangGraph, Strands, CrewAI, or similar agentic AI frameworks.
- Experience with production LLM evaluation frameworks such as LangSmith, Braintrust, or Ragas.
- Familiarity with healthcare or life sciences data, including IQVIA data structures, pharmaceutical workflows, payer data contracts, or similar environments.
Compensation and Benefits
- Benefits and compensation information were not provided in the available description.
Skills
Python, AWS, Terraform, Vpc Networking, IAM, Databricks, Delta Lake, Snowflake, Amazon S3, Mcp Servers, Agentic AI, Llm Applications, LangGraph, APIs, HIPAA
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