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 Qualifications 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 (SageMaker, 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 TensorFlow; 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 LangChain or LlamaIndex 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 Science, or related technical field; Master’s degree or equivalent advanced study preferred Travel for this role may be up to 80%, based on client and project needs Preferred Qualifications History of leading technical workstreams, mentoring engineers, or driving complex AI projects through full delivery lifecycle 2+ years as a senior individual contributor with full‑cycle project delivery and business development contribution History of supporting sales pursuits or business development initiatives Track record of engaging independently with executive or C‑suite stakeholders Established record of leading process improvement or innovation initiatives Confident presenter adaptable to any audience or format What We Look For Intellectual curiosity – asking thoughtful questions and seeking deeper understanding Attention to detail – noticing subtle issues and inconsistencies Analytical thinking – breaking down complex problems and thinking critically Tenacity – following issues through to resolution, even when challenging Strong communication – conveying ideas clearly to technical and non‑technical audiences The work‑life balance is supported with unlimited PTO, a flexible remote work policy, and a supportive environment that prioritizes sustainable, long‑term performance.

In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire. Crowe is not sponsoring for work authorization at this time. All persons hired will be considered under Crowe’s equal employment opportunity and will have no discrimination based on race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. #J-18808-Ljbffr