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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in AI and ML engineering, with a strong focus on setting enterprise-level standards and leading complex AI solution deliveries. Proven ability to mentor technical teams and influence strategic AI roadmaps in collaboration with senior leadership.
Highest-signal resume keywords
AI/ML Engineering LeadershipEnterprise Architecture GovernanceGenAI Patterns ExpertiseMentoring Staff and Senior EngineersAI Frameworks and Tools Proficiency
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI FrameworksAlgorithm OptimizationArtificial Neural Networks (ANNs)Deep LearningFeature EngineeringMachine Learning AlgorithmsMachine Learning Operations (MLOps)Model EvaluationModel TrainingModel Validation
Soft Skills
Technical LeadershipMentoringCollaborationInfluencing StakeholdersProblem Framing
Tools & Technologies
Google Cloud Platform (GCP)Microsoft Azure Data and AIClaude CodeGitHub CopilotDatabricks AI Assistant
Certifications & Qualifications
Bachelor's Degree in Computer ScienceMaster's or PhD in Computer Science or Machine Learning (Preferred)
Industry Keywords
Enterprise-Level StandardsAI-Native EngineeringDelivery Automation WorkflowsTechnical Design ReviewsStatistical and Causal Inference
Tech Stack
Tools & technologiesAzureCloudCyber SecurityGoogle Cloud PlatformSDLC
About the role
Key responsibilities & impact- Define and own enterprise-level development standards for AI solutions across GSK
- Set enterprise technical direction for AI-native engineering, including agentic workflows and delivery bot meshes
- Own the enterprise pattern library and reference platform
- Chair FDE technical design reviews and contribute to Enterprise AI architecture governance
- Own FDE delivery for a defined portfolio of strategic business units from intake to clean handoff
- Lead complex, precedent-setting engagements hands-on in problem framing, architecture, and prototype delivery
- Lead multi-FDE engagement teams across concurrent engagements
- Partner with Director- and VP-level business unit leaders to shape AI roadmaps and qualify high-value use cases
- Serve as technical authority and escalation point for architecture, ML, data science, and agentic engineering
- Mentor and grow Staff and Senior FDEs and shape technical career paths
- Lead senior technical interviews and calibrate the technical hiring bar
- Partner with Enterprise Architecture, Data Platforms, Cyber Security, and Tech functions
- Influence vendor strategy and tooling choices for AI models, agent frameworks, and developer tooling
- Represent GSK's AI engineering practice externally
- Embed Code Orange, Responsible AI, and Accountability Report requirements into development standards
- Report to Enterprise AI leadership on standards adoption, business unit delivery outcomes, and technical risk
Requirements
What you’ll need- 15 or more years of experience in software engineering and AI/ML engineering, including principal-level technical leadership across multiple teams or an enterprise
- Bachelor's degree in computer science, Software Engineering, or equivalent practical experience
- Demonstrated track record of setting engineering standards, reference architectures, or platforms adopted at organizational or enterprise scale
- Proven track record of taking AI ideas from sketch to working software and production at speed in customer-facing or embedded engineering roles, and leading delivery across a portfolio of business stakeholders
- Hands-on expertise in GenAI patterns, including agents, multi-agent orchestration, RAG, prompt engineering, evaluation, fine-tuning, and Model Context Protocol (MCP) integrations
- Enterprise-level expertise in AI, ML, and data science, including classical ML, deep learning, LLMs and foundation models, statistical and causal inference, and MLOps at scale
- Demonstrated daily use of AI coding agents such as Claude Code, GitHub Copilot, or Databricks AI Assistant as the primary mode of development
- Track record of architecting agentic delivery-automation workflows and delivery bot meshes and defining enterprise standards for their use
- Experience influencing and partnering with Director and VP-level business and technology leaders
- Prior responsibility for mentoring Staff- and Senior-level engineers and shaping senior technical career paths
- Preferred: Principal, Distinguished, or Head of Forward Deployed Engineering-level experience at an AI-native company or hyperscaler
- Preferred: Master's or PhD in Computer Science, Machine Learning, or a related quantitative field
- Skills: AI Frameworks, Algorithm Optimization, Artificial Neural Networks (ANNS), Computer Programming, Deep Learning, Feature Engineering, Google Cloud Platform (GCP) for Machine Learning, Machine Learning Algorithms, Machine Learning Operations, Microsoft Azure Data and AI, ML Frameworks, Model Evaluation, Model Training, Model Validation, Performance Optimizations, Software Development Life Cycle (SDLC)
Benefits
Comp & perks- Annual bonus
- Eligibility to participate in share based long term incentive program
- Health care and other insurance benefits for employee and family
- Retirement benefits
- Paid holidays
- Vacation
- Paid caregiver/parental and medical leave