Senior AI Engineer
Content + Source + Freshness • 16 Dec 2025 • 95% confidence
Offer value
Significant expertise required in advanced AI technologies and cloud architectures alongside proven leadership experience, making it a compelling opportunity in the AI field.
- Leading edge role in AI with high responsibility
- Competitive salary and significant career advancement prospects
- Involvement in transformative manufacturing projects
Pros
- Work with cutting-edge technologies in AI and manufacturing
- Strong opportunity for leadership and mentoring
- High market demand for roles involving Generative AI
Cons
- High competition for positions with substantial expertise requirements
- Potential high-stress environment due to project-related pressures
- Limited entry points for less experienced candidates
Who it's for
Senior / Lead • In-office / hybrid
Good fit
- Experienced AI engineers with strong leadership skills
- Professionals looking to innovate in manufacturing technology
- Candidates interested in collaborative, team-oriented environments
Not recommended for
- New graduates or candidates without AI experience
- Individuals who prefer purely remote work settings
- Those resistant to high-pressure project deadlines
Motivation fit
Key skills
About the job
• Lead architectural design and development of digital twin-driven generative/optimization models for process simulation, process scheduling, and predictive analytics in manufacturing.
• Develop modular, reusable Omniverse extensions; orchestrate pipelines integrating synthetic data generation, model training, validation, and deployment.
• Implement advanced optimization: multi-objective, non-linear, evolutionary, and constraint-based approaches for process efficiency (e.g., scheduling, logistics, yield predictions).
• Guide the integration of embedded AI & Gen AI solutions with real-time industrial data streams, ensuring high-throughput, high-availability, and low-latency deployment.
• Mentor teams in coding best practices, simulation-modelling, and deployment workflows using OpenUSD, Omniverse, and latest industrial AI stacks.
Requirements
- Strong experience with Generative AI models.
- Hands-on experience with Amazon bedrock and AWS AI-ML services
- Proficiency in building and managing Agentic AI systems using frameworks like LangChain, AutoGen or similar.
- Expertise on cloud-native architecture, LLMOps and MLOps tools.
- Familiarity with security, cost and governance best practices for Gen AI on cloud environments.
- Previous leadership in deploying ML pipelines and automation (CI/CD, containerization) at scale.
- Knowledge of quantization-aware training, model pruning, and NVIDIA-specific deployment tools (TensorRT, DeepStream SDK).
- Working knowledge of OpenUSD scene graphs, data schemas, and extension development for Omniverse Kit/Composer.
- Published research, open-source contributions, or patents in simulation-driven manufacturing AI or optimization.
- Familiarity with NVIDIA’s AI stack—RAPIDS, Triton Inference Server, TensorRT, cuOpt (or similar)
- Applied industrial AI expertise: root-cause analysis, predictive maintenance, digital twin calibration.
- Typically requires a Bachelor’s degree, preferably in computer science, Artificial Intelligence, Data Science, mathematics, or a similar field with quantitative coursework, and 7-10 years of professional experience in associated field is required, a Master’s degree and 4-6 years of experience, or a PhD and 1-3 years of experience in relevant field.
🔍 ATS Optimization Keywords
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Hard Skills
- Generative AI models
- Agentic AI systems
- cloud-native architecture
- LLMOps
- MLOps tools
- quantization-aware training
- model pruning
- OpenUSD
- Omniverse Kit
- NVIDIA-specific deployment tools
Soft Skills
- leadership
- mentoring
- coding best practices
- process efficiency
