Technology Lead, Agentic AI, R&D

Merck KGaA, Darmstadt, Germany

Technology Lead shaping AI-amplified research and development platforms to impact global patients. Leading architectural decisions and managing engineering standards in an innovative environment.

Posted 7/28/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $119,300 - $178,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and evolving production-grade agentic systems, with a strong focus on technical leadership, security practices, and effective communication across technical and non-technical stakeholders. Proficient in building scalable infrastructure and establishing engineering standards that support complex software systems.

Highest-signal resume keywords

Technical LeadershipDistributed SystemsAWS/Azure Cloud InfrastructureCI/CD Pipeline ManagementAgentic AI Platform Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Architectural Decision MakingAutomated Testing StrategiesProduction-Grade Software DevelopmentMulti-Agent AI SystemsLarge Language Model OrchestrationData GovernanceSecurity PracticesInput ValidationAuthorizationAuthentication

Soft Skills

Strong Communication SkillsMentoring

Tools & Technologies

CI/CD ToolsObservability ToolsSAFe Framework

Certifications & Qualifications

Master’s Degree in Computer SciencePh.D in Computer Science

Industry Keywords

Research and Development WorkflowsScientific Data EnvironmentsRegulated Data Contexts

Tech Stack

Tools & technologies

AWSAzureCloudDistributed Systems

About the role

Key responsibilities & impact

Own the architecture and evolution of a production-grade agentic system that enables autonomous experiment design and automated reporting at scale.
Define and uphold engineering standards through hands-on code review, pairing, and concrete examples.
Build and maintain shared agentic infrastructure that allows multiple programs to contribute specialized agents within a single governed platform.
Own platform design decisions spanning data sources, access controls, agent composition patterns, scalability, and technical debt management.
Lead the technical roadmap, guiding both internal engineers and external contractors.
Ship production code, own CI/CD pipelines and observability, and drive robust testing and security practices for sensitive research data.
Translate complex research and development requirements into reliable technical solutions and communicate them clearly to both technical and non-technical stakeholders.
Mentor developers and help the team navigate the evolving AI landscape.

Requirements

What you’ll need

Master’s degree in computer science, data science, chemistry, materials science, or a related field with 3+ years of experience Or Ph.D in computer science, data science, chemistry, materials science, or a related field with 2+ years of experience.
Demonstrated experience in technical leadership, including making architectural decisions under uncertainty and guiding cross-functional engineering teams.
Solid understanding of distributed systems, APIs, and cloud infrastructure on AWS and/or Azure.
Security-first mindset with hands-on knowledge of authentication, authorization, input validation, and AI-specific risks such as prompt injection and data leakage.
Experience establishing engineering standards, automated testing strategies, and maintainable code practices that scale across teams and contributors.
Proven track record of building and operating complex, production-grade software systems end to end, including ownership of CI/CD, observability, and on-call operations.
Experience designing or operating multi-agent or agentic AI platforms in a production environment.
Familiarity with large language model (LLM) orchestration frameworks and patterns for agent composition and governance.
Experience working within SAFe or similar scaled agile delivery frameworks.
Strong communication skills with the ability to bridge software engineering and non-technical research or product stakeholders.
Familiarity with research and development workflows, scientific data environments, or regulated data contexts where security and data governance are critical.

Benefits

Comp & perks

health insurance
paid time off (PTO)
retirement contributions