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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, deploying, and maintaining end-to-end ML systems and production-grade LLM applications, with a strong focus on leading cross-functional initiatives and fostering technical excellence within engineering teams.
Highest-signal resume keywords
End-To-End ML Systems DesignProduction-Grade LLM ApplicationsAgentic AI Systems ArchitectureCross-Functional Initiative LeadershipCode Quality and System Reliability
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning EngineeringLLM IntegrationInfrastructure as CodeMulti-Agent CoordinationStateful Workflow OrchestrationAdvanced Reasoning StrategiesModel Fine-TuningAutomated Evaluation FrameworksDeep-Dive Code ReviewsObservability
Soft Skills
CollaborationTechnical ExcellenceLeadership
Tools & Technologies
Vector DatabasesDistributed ComputingGPU Optimization
Industry Keywords
AI SolutionsAutonomous Decision LoopsComplex Action ExecutionRAG SystemsCombinatorial Optimization AlgorithmsQuantum Computing
About the role
Key responsibilities & impact- Design production-grade LLM architectures and autonomous Agentic AI systems
- Apply exploratory research and practical implementation to solve complex, real-world problems
- Drive customer value through AI solutions
- Collaborate closely with scientists, engineers, and customers
- Design, deploy, and maintain end-to-end ML systems and production-grade LLM applications at scale
- Lead the transition from prototypes to production-hardened services
- Lead cross-functional initiatives and translate business objectives into technical roadmaps
- Conduct deep-dive code and architecture reviews
- Help foster a culture of technical excellence and improve high-performing engineering teams
Requirements
What you’ll need- Proven track record of designing, deploying, and maintaining end-to-end ML systems and production-grade LLM applications at scale
- Experience owning the transition from prototype to production-hardened service
- Minimum of 5 years of professional experience in ML and AI Engineering
- Robust system design, observability, safety/guardrails, and infrastructure as code
- Hands-on experience architecting Agentic AI systems, including multi-agent coordination, stateful workflow orchestration, autonomous decision loops, and complex action execution
- Strong background in LLM integration, including function calling/tool-use, structured output parsing, and advanced reasoning strategies
- Ability to lead cross-functional initiatives, translate business objectives into technical roadmaps, and uphold high standards for code quality and system reliability
- Experience leveling up high-performing engineering teams, conducting deep-dive code/architecture reviews, and fostering technical excellence
- Deep experience in LLM infrastructure, advanced RAG systems, model fine-tuning, agent memory architectures, and automated evaluation frameworks is a plus but not a requirement
- Proficiency with vector databases, distributed computing, and GPU optimization is a plus but not a requirement
- Familiarity with combinatorial optimization algorithms or the intersection of ML and Quantum Computing is a plus but not a requirement
Benefits
Comp & perks- Cookie consent and website privacy options are described; no role-specific benefits are stated