NVIDIA

1 week ago

Engagement Tech Lead, Agentic AI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Drive technical engagements with strategic enterprise ISV partners, aligning objectives and adoption plans with their software platform architects. Stay hands-on designing and shipping production agentic AI systems using NVIDIA’s stack and open tools, covering RAG, inference, and multi-agent long-horizon workflows. Lead technical strategy and execution, share learnings with Product/Engineering/Research, and serve as the partner voice to shape roadmap and agentic platform architecture.

Location: Santa Clara, United States

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Lead technical engagement with a focused set of ISV partners, setting objectives, timelines, and adoption plans with their software platform architects.
•Design and ship production agentic systems and reference architectures across NVIDIA’s stack and open tools, implementing RAG, inference, and multi-agent workflows.
•Run technical strategy and execution by tracking adoption, maintaining cadence, and sharing learnings with Product, Engineering, Research, and Solution Architecture teams.
•Build expertise across the agentic AI lifecycle, including fine-tuning, reasoning, advanced RAG, multi-agent workflows, evaluation/observability, and production inference.
•Act as the partner voice inside NVIDIA, translating partner needs and architectures into roadmap input across teams.

Pay and Benefits

Salary: USD 224,000 - 431,250 annually

Equity and Bonus:Equity

Key Requirements

•12+ years in technical Product, Engineering, or Solutions roles across enterprise software and production AI, including customer- or partner-facing work.
•Masters or PhD in Computer Science, Electrical Engineering, or equivalent experience.
•Strong AI/ML and deep learning foundation with hands-on experience building enterprise-grade GenAI systems, including advanced RAG, multi-agent architectures, and production LLM deployments.
•Ability to lead technical engagements with customers and earn trust of senior engineers, architects, and executives while bringing cross-functional teams along.
•Proficiency in Python and PyTorch and the modern agent/LLM stack, plus solid grasp of enterprise deployment (MLOps/LLMOps, Kubernetes, Docker, and security/governance).

Experience:12+ yearsEnterprise softwareProduction AIGenAI

Education:PhD / Doctorate in Computer Science, Electrical Engineering

Skills:Technical leadershipCommunicationCross-functional collaborationExecution rigorPartner influence

Tech Stack:Generative AIAgentic AIRAGInferenceMulti-agentNeMoNemotronNeMo Agent ToolkitNIMDynamoTensorRT-LLMVLLMLangChainVector DBsMCPA2APEFTSFTRLMLOps

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 week ago

Engagement Tech Lead, Agentic AI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: Santa Clara, United States

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Lead technical engagement with a focused set of ISV partners, setting objectives, timelines, and adoption plans with their software platform architects.
•Design and ship production agentic systems and reference architectures across NVIDIA’s stack and open tools, implementing RAG, inference, and multi-agent workflows.
•Run technical strategy and execution by tracking adoption, maintaining cadence, and sharing learnings with Product, Engineering, Research, and Solution Architecture teams.
•Build expertise across the agentic AI lifecycle, including fine-tuning, reasoning, advanced RAG, multi-agent workflows, evaluation/observability, and production inference.
•Act as the partner voice inside NVIDIA, translating partner needs and architectures into roadmap input across teams.

Pay and Benefits

Salary: USD 224,000 - 431,250 annually

Equity and Bonus:Equity

Key Requirements

•12+ years in technical Product, Engineering, or Solutions roles across enterprise software and production AI, including customer- or partner-facing work.
•Masters or PhD in Computer Science, Electrical Engineering, or equivalent experience.
•Strong AI/ML and deep learning foundation with hands-on experience building enterprise-grade GenAI systems, including advanced RAG, multi-agent architectures, and production LLM deployments.
•Ability to lead technical engagements with customers and earn trust of senior engineers, architects, and executives while bringing cross-functional teams along.
•Proficiency in Python and PyTorch and the modern agent/LLM stack, plus solid grasp of enterprise deployment (MLOps/LLMOps, Kubernetes, Docker, and security/governance).

Experience:12+ yearsEnterprise softwareProduction AIGenAI

Education:PhD / Doctorate in Computer Science, Electrical Engineering

Skills:Technical leadershipCommunicationCross-functional collaborationExecution rigorPartner influence

Tech Stack:Generative AIAgentic AIRAGInferenceMulti-agentNeMoNemotronNeMo Agent ToolkitNIMDynamoTensorRT-LLMVLLMLangChainVector DBsMCPA2APEFTSFTRLMLOps

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor