Senior AI Engineer

About the Role

Senior AI Engineer 1 (Senior Staff) leads the development of advanced AI and machine learning systems with a high degree of autonomy. This role partners closely with architects and product stakeholders to design end-to-end AI solutions, optimize model performance, and ensure scalable, cloud-native deployment. The Senior Staff engineer drives technical decision-making, performs in-depth system analysis, and resolves complex engineering challenges across data, model, and infrastructure layers.

The role is responsible for producing high-quality, well‑architected solutions and setting technical standards that elevate engineering practices across the team. Senior AI Engineer 1 mentors junior engineers, supports cross‑team collaboration, and contributes significantly to roadmap planning.

• Design and implement complex AI/ML systems, pipelines, and model‑serving architectures for enterprise workloads
• Lead development of reusable frameworks, libraries, and tools to accelerate AI engineering across teams
• Analyze large‑scale datasets, model telemetry, and inference performance to drive optimization strategies
• Architect distributed training and model evaluation workflows that improve reliability and accuracy
• Collaborate with senior stakeholders to define solution approaches, technical requirements, and feasibility assessments
• Guide junior and mid‑level engineers through design reviews, code reviews, and hands‑on technical mentorship
• Implement advanced automated testing, including stress testing, bias detection, non‑regression testing, and quality evaluations
• Troubleshoot complex pipeline failures, infrastructure errors, and distributed system bottlenecks
• Document architectural decisions, engineering patterns, and best practices to elevate organizational knowledge
• Optimize performance across all stages of model lifecycle, including preprocessing, training, and inference
• Participate in roadmap discussions and provide expert‑level technical recommendations for future AI capabilities
• Ensure alignment with security, compliance, data governance, and responsible AI guidelines
• Research new generative AI, machine learning, and cloud technologies to evaluate applicability to enterprise use cases
• Contribute to incident response and operational support for deployed AI systems

• 4–6 years of professional AI/ML engineering or software engineering experience
• Deep proficiency in Python, ML frameworks, and cloud‑native engineering
• Strong understanding of distributed systems, data pipelines, and model optimization; ability to lead technical designs and perform advanced debugging
• Advanced hands‑on experience with AWS, Azure, or Google Cloud; strong containerization expertise (Docker); production deployment using Kubernetes (EKS/AKS/GKE)
• Proficiency with Terraform and infrastructure automation; deep experience with cloud ML platforms (Sage Maker, Vertex AI, Azure ML)
• Hands‑on background with GPU/accelerator workflows; building and optimizing distributed training jobs; strong knowledge of observability and monitoring tools
• Expertise with PyTorch and/or Tensor Flow; advanced experience fine‑tuning transformer architectures using Hugging Face
• Hands‑on experience designing RAG systems with vector databases including Pinecone, Weaviate, or FAISS; building GenAI microservices using Lang Chain or Llama Index
• Demonstrated success evaluating and integrating LLM APIs (OpenAI, Azure OpenAI, Gemini); hands‑on implementing PEFT and LoRA/QLoRA fine‑tuning techniques
• Skilled in designing LLM evaluation suites covering quality, safety, latency, and bias; track record optimizing inference at scale
• Proficiency with low‑code platforms including Microsoft Power Platform (Power Apps, Power Automate, AI Builder, Copilot Studio); experience developing APIs and SDKs that enable low‑code AI consumption and building agents with multi‑step reasoning and tool orchestration
• Effective communication for cross‑functional technical alignment; demonstrated ability to work independently and handle complex problems
• Demonstrated ability to own a complete workstream lifecycle with minimal supervision
• Bachelor’s degree in Computer Science, Engineering, Data…