B
AI Engineer
Burtch Works
6 hours ago
Full-time
On-site
Chicago, Illinois, United States
AI Solutions Engineer – Life Sciences
Employment Type:
Full-Time | Direct Hire
Industry:
Life Sciences / Pharmaceutical
Work Arrangement:
Remote-Friendly
Technology Stack:
Google Cloud Platform (GCP), BigQuery, Vertex AI
The Opportunity Our client is a life sciences company providing outsourced commercialization services—including launch strategy, distribution, marketing, and analytics—to small and mid‑sized pharmaceutical companies. The company is expanding its new AI Agency, which develops production‑ready AI solutions for pharmaceutical and healthcare marketing. We are seeking an experienced, hands‑on AI Solutions Engineer to work directly with client teams, collaborating with their engineers and stakeholders to translate business needs into scalable, production‑ready systems. This is a highly technical, client‑facing engineering role focused on building and deploying solutions— not a consulting‑only position.
Key Responsibilities Technical Discovery & Solution Design
Lead technical discovery sessions with client business, IT, security, data, and operations teams.
Translate business requirements into technical specifications, system architectures, and actionable engineering plans.
Serve as a trusted technical advisor, identifying risks, dependencies, and assumptions early.
Communicate complex AI, data, and integration concepts to both technical stakeholders and executive audiences.
AI Engineering & Development
Design, develop, and deploy AI workflows using large language models (LLMs), retrieval-augmented generation (RAG), agent orchestration, and structured outputs.
Incorporate human approval checkpoints, evaluation frameworks, and compliance controls into AI solutions.
Develop client‑specific configurations, connectors, and deployment assets on a shared GCP platform while maintaining architectural integrity.
Enterprise Integrations & Data Engineering
Build and maintain integrations using APIs, webhooks, and event‑driven architectures.
Integrate AI solutions with enterprise platforms such as:
Veeva Vault / PromoMats
Salesforce Marketing Cloud
Microsoft SharePoint
Adobe Experience Cloud
Snowflake
Implement secure authentication, secrets management, error handling, and audit controls.
Develop ETL/ELT data pipelines supporting reporting, analytics, and AI‑driven workflows.
Production Deployment & Operations
Rapidly prototype solutions to validate technical feasibility.
Transition prototypes into stable, scalable production environments.
Implement CI/CD pipelines, containerization, automated testing, and monitoring.
Troubleshoot production issues and continuously improve system reliability and performance.
Required Qualifications Experience & Technical Expertise
9+ years of experience in software engineering, solutions engineering, or technical consulting.
Strong hands‑on experience with Google Cloud Platform (GCP), specifically BigQuery and Vertex AI.
At least 2 years of experience building, orchestrating, and deploying AI agents into production environments.
Advanced proficiency in Python and at least one additional programming language, such as JavaScript/TypeScript, C#, Java, or another .NET language.
Strong experience with enterprise APIs, data pipelines, event‑driven architectures, authentication, security, and system integrations.
AI & Machine Learning
Practical knowledge of large language models (LLMs) and prompt engineering.
Experience with retrieval‑augmented generation (RAG), embeddings, and vector databases.
Understanding of AI evaluation, observability, guardrails, and production monitoring.
Ability to build AI solutions that incorporate human oversight and regulatory compliance requirements.
Development & DevOps
Experience with Git, CI/CD, and automated testing.
Hands‑on experience with Docker and Kubernetes.
Proven ability to take solutions from initial concept through production deployment.
Professional Skills
Strong communication and stakeholder management skills.
Ability to work directly with client engineering and business teams.
Experience translating complex business requirements into practical technical solutions.
Comfortable working in regulated environments with security, compliance, and audit requirements.
Ideal Candidate The ideal candidate combines deep software engineering expertise with practical experience deploying AI agents and workflows in enterprise environments. They are comfortable engaging directly with clients, designing scalable architectures, writing production‑quality code, and navigating the security and compliance requirements of the life sciences industry.
This role is best suited for a hands‑on engineer who enjoys building real‑world AI solutions and taking ownership from technical discovery through production deployment. ee in a technical field or equivalent practical experience. - Strong communicator who can turn client needs into clear requirements and is comfortable with both engineers and executives.
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Full-Time | Direct Hire
Industry:
Life Sciences / Pharmaceutical
Work Arrangement:
Remote-Friendly
Technology Stack:
Google Cloud Platform (GCP), BigQuery, Vertex AI
The Opportunity Our client is a life sciences company providing outsourced commercialization services—including launch strategy, distribution, marketing, and analytics—to small and mid‑sized pharmaceutical companies. The company is expanding its new AI Agency, which develops production‑ready AI solutions for pharmaceutical and healthcare marketing. We are seeking an experienced, hands‑on AI Solutions Engineer to work directly with client teams, collaborating with their engineers and stakeholders to translate business needs into scalable, production‑ready systems. This is a highly technical, client‑facing engineering role focused on building and deploying solutions— not a consulting‑only position.
Key Responsibilities Technical Discovery & Solution Design
Lead technical discovery sessions with client business, IT, security, data, and operations teams.
Translate business requirements into technical specifications, system architectures, and actionable engineering plans.
Serve as a trusted technical advisor, identifying risks, dependencies, and assumptions early.
Communicate complex AI, data, and integration concepts to both technical stakeholders and executive audiences.
AI Engineering & Development
Design, develop, and deploy AI workflows using large language models (LLMs), retrieval-augmented generation (RAG), agent orchestration, and structured outputs.
Incorporate human approval checkpoints, evaluation frameworks, and compliance controls into AI solutions.
Develop client‑specific configurations, connectors, and deployment assets on a shared GCP platform while maintaining architectural integrity.
Enterprise Integrations & Data Engineering
Build and maintain integrations using APIs, webhooks, and event‑driven architectures.
Integrate AI solutions with enterprise platforms such as:
Veeva Vault / PromoMats
Salesforce Marketing Cloud
Microsoft SharePoint
Adobe Experience Cloud
Snowflake
Implement secure authentication, secrets management, error handling, and audit controls.
Develop ETL/ELT data pipelines supporting reporting, analytics, and AI‑driven workflows.
Production Deployment & Operations
Rapidly prototype solutions to validate technical feasibility.
Transition prototypes into stable, scalable production environments.
Implement CI/CD pipelines, containerization, automated testing, and monitoring.
Troubleshoot production issues and continuously improve system reliability and performance.
Required Qualifications Experience & Technical Expertise
9+ years of experience in software engineering, solutions engineering, or technical consulting.
Strong hands‑on experience with Google Cloud Platform (GCP), specifically BigQuery and Vertex AI.
At least 2 years of experience building, orchestrating, and deploying AI agents into production environments.
Advanced proficiency in Python and at least one additional programming language, such as JavaScript/TypeScript, C#, Java, or another .NET language.
Strong experience with enterprise APIs, data pipelines, event‑driven architectures, authentication, security, and system integrations.
AI & Machine Learning
Practical knowledge of large language models (LLMs) and prompt engineering.
Experience with retrieval‑augmented generation (RAG), embeddings, and vector databases.
Understanding of AI evaluation, observability, guardrails, and production monitoring.
Ability to build AI solutions that incorporate human oversight and regulatory compliance requirements.
Development & DevOps
Experience with Git, CI/CD, and automated testing.
Hands‑on experience with Docker and Kubernetes.
Proven ability to take solutions from initial concept through production deployment.
Professional Skills
Strong communication and stakeholder management skills.
Ability to work directly with client engineering and business teams.
Experience translating complex business requirements into practical technical solutions.
Comfortable working in regulated environments with security, compliance, and audit requirements.
Ideal Candidate The ideal candidate combines deep software engineering expertise with practical experience deploying AI agents and workflows in enterprise environments. They are comfortable engaging directly with clients, designing scalable architectures, writing production‑quality code, and navigating the security and compliance requirements of the life sciences industry.
This role is best suited for a hands‑on engineer who enjoys building real‑world AI solutions and taking ownership from technical discovery through production deployment. ee in a technical field or equivalent practical experience. - Strong communicator who can turn client needs into clear requirements and is comfortable with both engineers and executives.
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