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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in GPU training infrastructure, including proficiency in Python and shell scripting, and experience with multi-node distributed training using PyTorch and Ray Train. Capable of designing performance benchmarks and implementing scalable AI architectures while effectively communicating technical decisions and collaborating with cross-functional teams.
Highest-signal resume keywords
GPU Training InfrastructurePython MasteryMulti-Node Distributed TrainingGoogle Cloud (Vertex AI)Performance Benchmark Design
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Work with the client's infrastructure team on the GKE training platform, including GPU node pools, job queueing and scheduling, quota, and capacity
- Diagnose and fix platform failures at the root cause
- Tune multi-node GPU networking, including RDMA/GPUDirect and NCCL, for training throughput
- Run, scale, and debug multi-node distributed training with PyTorch and Ray Train
- Profile jobs and remove bottlenecks across compute, memory, input pipelines, and interconnect
- Design and run performance benchmarks for the cluster and training stack
- Build and maintain model evaluation harnesses
- Instrument training and the platform with telemetry and monitoring
- Maintain end-to-end pipeline tests against real user workloads
- Proactively identify issues, address gaps, propose solutions, communicate status and risks, and support scientists using the platform
- Explain and defend design decisions to client engineers and scientists
- Adapt effectively as client priorities and team structure change
- Develop document intelligence and retrieval-augmented generation (RAG) features from prototype through scalable production services
- Translate client business needs into high-performance AI architectures
Requirements
What you’ll need- 5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training
- Mastery of Python and shell scripting
- Extensive experience with Google Gemini, GPT-4, or LLaMA
- Deep knowledge of prompt engineering and fine-tuning
- Expertise in vector databases, including Vertex AI Vector Search and pgvector
- Experience implementing semantic search
- Hands-on experience with Google Cloud (Vertex AI) and scalable software architectures
- Proficiency in LangChain, LlamaIndex, or similar orchestration layers
- Strong Python software engineering, including production-quality code, tests, and code review
- Experience with Kubernetes on Google Cloud, GPU node pools, job queueing, and capacity/quota management
- Personal experience running and debugging multi-node distributed training jobs with PyTorch and Ray Train
- Experience with GPUDirect/RDMA, NCCL tuning, and multi-node GB200 or H200 topology
- Experience designing performance benchmarks and model evaluations
- Ability to explain system operation and rationale without relying on AI coding tools
- Track record of visible ownership, communication, and accountability on a demanding team
- Healthcare or life-sciences data experience and HIPAA experience are nice to have
- Observability and telemetry for training platforms are nice to have
Benefits
Comp & perks- Comprehensive Health Insurance
- Paid Leave (Vacation/PTO)
- Paid Holidays
- Sick Leave
- Parental Leave
- Bereavement Leave
- 401(k) Employer Match
- Employee Referral Bonuses
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonShell ScriptingPrompt EngineeringFine-TuningVector DatabasesSemantic Search ImplementationKubernetesGPUDirect/RDMANCCL TuningModel Evaluation
Soft Skills
CommunicationAccountabilityProblem-SolvingAdaptabilityOwnership
Tools & Technologies
Google GeminiGPT-4LLaMALangChainLlamaIndexVertex AI Vector SearchTelemetryMonitoring
Industry Keywords
Healthcare Data ExperienceLife-Sciences Data ExperienceHIPAA Experience