Senior AI Engineer

Crowe•Nashville, TN

5d•$74,100 - $147,800•Remote

About The Position

Crowe Studio is a business unit dedicated to helping clients scale smarter, transform faster, and lead in a platform-driven economy. Built for speed and flexibility, Crowe Studio operates outside the constraints of traditional professional services delivery models, giving clients access to cross-disciplinary innovation, embedded AI capabilities, and global delivery resources—all in service of solving complex business challenges in faster, smarter ways. Through Crowe Studio, the firm provides clients with an innovation partner focused on rapid execution, deep technology integration, and high-impact results. We’re building the next generation of business models for professional services, where human expertise and AI are embedded within clients’ operations to drive ongoing impact, not just deliver isolated projects. As a member of Crowe Studio, you will help distinguish Crowe in the market and drive the firm’s technology and innovation strategy. The future is powered by AI—come build it with us. The Forward Deployed Engineering (FDE) Practice drives AI transformation through embedded consulting and engineering, enabling clients to accelerate delivery, scale solutions, and capture value faster. Our approach helps companies prove AI in their own environments, delivering results in weeks, not months. We embed our Forward Deployed Engineers side-by-side with client teams to spot opportunities, make AI concepts real, and deploy solutions rapidly. Unlike traditional consulting approaches that rely on documented requirements and lengthy development cycles, FDEs blend engineering, product thinking, workflow design, and business understanding to achieve shared, outcome-driven ownership until measurable results are achieved. Our engagements range from 2-hour AI Immersion Labs that turn AI noise into real use cases, to multi-week Proof of Value Sprints that prototype and deploy game-changing AI solutions. We help companies de-risk AI adoption while demonstrating solid ROI. We invest in expertise. You’ll have the time, space, and support to go deep on client engagements and build lasting technical and strategic mastery. You’ll work with cross-functional teams and client teams as a trusted advisor and domain expert. We believe in continuous growth. Our team is committed to professional development and knowledge-sharing. We protect balance. Our distributed team culture is grounded in trust and flexibility. We offer unlimited PTO, a flexible remote work policy, and a supportive environment that prioritizes sustainable, long-term performance. Role Overview: The 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. This position establishes itself as a strong individual contributor with deep technical expertise in AI engineering and generative AI technologies.

Requirements

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
Basic ML/AI literacy (training vs inference, knowledge cutoffs, LLM fundamentals)
Prompt engineering and instruction hierarchies
Context window and context management
Model selection and capabilities
Fine-tuning vs prompting vs RAG
Hallucination and grounding strategies
Guardrails and output validation
Evals and testing approaches
Security and PII awareness
Responsible AI and governance
LLM APIs and integration standards
RAG and vector search
Vector databases and embeddings
Agentic workflows and tool use
Cost and performance awareness
Common enterprise use cases
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

Nice To Haves

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

Responsibilities

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