IntegriChain
Life Sciences
AIEngineer,ForwardDeployed
“AI Engineer, Forward Deployed at IntegriChain. Skills: AI/LLM Application Development, Embedded Engineering, Consultative Problem Solving. Embed with internal departments. Understand workflows and pain points”
What You'll Achieve.
Drive efficiency, accuracy, and scale with AI; Deliver AI-powered solutions; Ensure reliability and performance of AI models; Inform the product roadmap; Support scaling to additional teams
Industry & Context.
Troubleshoot and debug AI models and pipelines; Identify and resolve coverage and availability hurdles; Manage pricing and forecasting complexity
Candidates must reside in Pennsylvania, New Jersey, or New York, Be within a reasonable travel distance of our Philadelphia office, Regular in-person collaboration is required, Must have valid work authorization that does not require sponsorship
What They're Looking For.
Must Have
5+ years of software engineering or data application development experience, hands-on experience building AI/LLM-enabled applications, Demonstrated ability to work in an embedded, consultative capacity, understanding of LLM application patterns, Experience creating agents that can select tools, execute backend functions, summarize tool outputs, and continue multi-step workflows, Hands-on experience with advanced AI coding tools such as Cursor, Claude, Codex, GitHub Copilot, or similar developer-assistance tools, Python development skills, experience building modular, maintainable application code, Experience designing chat-based user interfaces or conversational workflows for business users, Understanding of token management, context-window design, LLM cost drivers, and model performance tradeoffs, Experience integrating AI applications with data platforms, APIs, SQL engines, or enterprise backend services, Ability to work cross-functionally, translating business and operational workflows into AI-assisted product features, communicating technical concepts to non-technical stakeholders, Comfortable operating as a hands-on individual contributor in a fast-moving, ambiguous environment with shifting priorities, troubleshooting and debugging skills for AI models and pipelines operating in live production environments
Nice to Have
Prior experience in a forward-deployed engineer, solutions engineer, or embedded technical consultant role, Experience working alongside operational or managed services teams to implement technology solutions, Experience with Snowflake Cortex Analyst, Cortex Complete, semantic models, or similar enterprise AI/data platform capabilities, Experience with Streamlit, FastAPI, React, or similar frameworks for AI/data application development, Experience deploying AI applications or services on AWS using containers, serverless components, managed secrets, IAM, and observability tooling, Experience with tool schema standards, structured JSON outputs, validation, and safe execution patterns, Experience in life sciences, healthcare, pharma commercialization, MDM, patient data, channel data, or commercial data platforms, Exposure to data visualization, analytics workflows, SQL generation, semantic layers, or natural-language-to-SQL products, Familiarity with evaluation frameworks, regression testing for prompts, and quality monitoring for AI features
What You'll Do.
Embed with internal departments
Understand workflows and pain points
Act as on-the-ground AI expert
Translate operational needs into AI requirements
Lead end-to-end AI solution implementation
Provide hands-on troubleshooting and support
Drive change management and adoption
Document operational AI use cases
Design and build LLM-powered features
Implement agent loops
Define and maintain tool schemas
Build orchestration logic
Create traceable AI experiences
Develop domain-aware system prompts
Optimize prompts for reliability
Understand LLM context windows
Monitor and improve model performance
Apply token and cost optimization techniques
Build production-quality chat interfaces
Integrate chart recommendations
Design graceful error handling
Partner with SRE and security teams
Design and support AI application deployment patterns in AWS
Implement backend services
Use Git-based development
Contribute to reusable AI engineering standards
How You'll Work.
Team & Collaboration
Partner directly with non-engineering business teams; Bridge communication between product engineering and internal stakeholders; Participate in team standups, process reviews, and strategic planning sessions; Collaborate with product and operational teams; Partner with SRE and security teams; Work with DevOps/SRE teams
Communication Scope
Translating business and operational workflows into AI-assisted product features; Communicating technical concepts to non-technical stakeholders
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