
Staff Developer/Architect – Next Gen AI
- Markham, ON
- Permanent
- Full-time
- Architect, prototype, and productionize scalable AI systems, with an emphasis on LLMs, simulation-aware models, and hybrid AI pipelines.
- Lead AI integration into core engineering tools and simulation frameworks, ensuring robustness, interpretability, and physical relevance of outputs.
- Evaluate and define the appropriate use of RAG systems, fine-tuning vs. zero/few-shot learning strategies, and feedback loops for continuous improvement.
- Drive forward-thinking initiatives involving multi-agent AI systems, context-aware simulation orchestration, or generative design techniques.
- Develop and operationalize full-stack AI pipelines using MLOps practices (e.g., Docker, Kubernetes, FastAPI, MLFlow, cloud-native services).
- Define strategies for large-scale data ingestion, embedding generation, retrieval tuning, and prompt optimization in production environments.
- Ensure scalability, reproducibility, and performance of deployed models through well-defined evaluation, monitoring, and retraining mechanisms.
- Serve as a key technical liaison between simulation teams, software development, platform/cloud architects, HW teams and AI research teams.
- Translate complex engineering needs into actionable AI solutions, balancing innovation with stability and traceability.
- Help define and evolve the technical roadmap for AI within GM’s digital twin and simulation ecosystem.
- Mentor engineers and data scientists, enabling growth in areas such as model architecture, deployment practices, and responsible AI.
- Establish and champion engineering best practices, coding standards, and documentation norms for AI systems across teams.
- Participate in technical reviews, external publications, or internal tech talks to scale knowledge and influence strategy.
- Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, or related field (PhD preferred, particularly in NLP, simulation AI, or reinforcement learning).
- 7+ years of experience building and deploying advanced machine learning or deep learning systems in production.
- Demonstrated expertise with LLMs, transformer architectures, AI agents, or simulation-integrated models.
- Strong experience in Python and major ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace Transformers).
- Experience with retrieval-augmented generation (RAG), prompt engineering, and embedding optimization.
- Proven ability to lead technical direction and deliver production-ready AI systems at scale.
- Experience with MLOps tools and deploying models via containerized microservices on cloud platforms.
- Experience in automotive or physical systems simulation domains.
- Familiarity with co-simulation frameworks, physical modeling (e.g., Simulink, Modelica), or system-level calibration workflows.
- Knowledge of optimization techniques such as PSO, GA, or MDO in the context of AI/simulation fusion.
- Contributions to open-source AI tools or published research in NLP, agents, or simulation-integrated AI.
- Visionary thinking: You identify and pursue novel AI applications in engineering workflows.
- Strategic ownership: You drive initiatives from concept to integration, influencing cross-org direction.
- Cross-domain fluency: You connect simulation, embedded systems, and data science to deliver tangible value.
- Commitment to mentorship: You uplift others and scale your expertise across the team.