Lead Solutions Engineer

About the Role

The Lead Intelligent Automation Engineer is a senior, hands‑on technical builder‑leader accountable for delivering AI and intelligent automation solutions end‑to‑end, from problem framing through production deployment and sustained value realization. The role personally designs, builds, and scales large‑language‑model‑based and agent‑based automations while owning solution architecture, technical quality, delivery outcomes, and enterprise standards.

• Hands‑On AI & Intelligent Automation Development
  • Design, build, and maintain AI prompts, prompt libraries, LLM‑based agents, copilots, chatbots, automation scripts, and end‑to‑end intelligent workflows.
  • Lead rapid prototyping and experimentation; convert successful pilots into scalable, production‑grade solutions.
  • Own day‑to‑day technical health of deployed solutions, including monitoring, troubleshooting, performance tuning, and reliability improvements.
  • Build and maintain robust integrations with enterprise platforms using APIs, services, data pipelines, and workflow orchestration tools.
• End‑to‑End Solution Architecture & Delivery Ownership
  • Serve as the end‑to‑end AI solution architect for assigned initiatives, making architectural decisions and implementing them hands‑on.
  • Define and standardize solution patterns for agent architectures, integration and data flow design, and environmental promotion and deployment strategies.
  • Own delivery planning with clearly defined value metrics, success criteria, and timelines.
  • Proactively identify technical risks, trade‑offs, and dependencies; drive resolution.
• Use Case Execution & Enterprise Impact
  • Lead execution for high‑impact, enterprise‑level AI and automation use cases, translating loosely defined business problems into durable, production‑ready AI solutions.
  • Typical use cases include LLM‑based agents for case intake, triage, summarization, and decision support; intelligent document processing across emails, PDFs, forms, and unstructured content; workflow automation for finance, operations, compliance, research, or shared services; embedded AI assistants integrated into enterprise systems.
  • Partner with business owners to validate outputs, refine logic, and ensure solutions are adopted, trusted, and operationalized.
• Governance, Security & Compliance
  • Ensure all solutions comply with enterprise AI governance standards, ethical AI principles, and corporate policies.
  • Design and implement secure‑by‑default solutions, including documentation, traceability, monitoring, and audit readiness.
  • Anticipate and mitigate risks related to data privacy, access control, model behavior, hallucinations, bias, and operational resilience.
  • Partner with architecture, security, and data governance teams for compliant solution design, particularly within regulated environments.
• Evangelism, Enablement & Technical Leadership
  • Engage business stakeholders, translating opportunities into concrete solution concepts and demonstrating value through working solutions.
  • Produce high‑quality technical documentation, user guides, and SOPs.
  • Lead hands‑on enablement sessions, workshops, and knowledge transfer to drive adoption.
  • Act as a technical mentor and thought leader, influencing standards, patterns, and best practices across AI and automation initiatives.

• Bachelor’s Degree required;
  Master’s Degree preferred.
• At least 7 years of experience in machine learning.
• Advanced knowledge of generative AI, machine learning, natural language processing, agentic frameworks, and AI solution architectures.
• Strong understanding of AI governance, MLOps, and enterprise risk management.
• Healthcare, life sciences, or clinical research technology domain knowledge.
• Proven experience delivering complex, production‑grade AI and automation solutions as a hands‑on builder.
• Deep proficiency in prompt engineering and applying generative AI to enterprise workflows.
• Strong programming skills (Python and/or JavaScript) with experience in APIs, data transformation, version control, and deployment practices.
• Demonstrated experience building agentic AI solutions, including RAG architectures, orchestration frameworks, and tool‑calling patterns.
• Expe…