Senior AI Engineer

Crowe•Washington, DC

40d

About The Position

Your Journey at Crowe Starts Here: At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you’re trusted to deliver results and make an impact. We embrace you for who you are, care for your well-being, and nurture your career. Everyone has equitable access to opportunities for career growth and leadership. Over our 80-year history, delivering excellent service through innovation has been a core part of our DNA across our audit, tax, and consulting groups. That’s why we continuously invest in innovative ideas, such as AI-enabled insights and technology-powered solutions, to enhance our services. Join us at Crowe and embark on a career where you can help shape the future of our industry. Job Description: About Crowe AI Transformation Everything we do is about making the future of human work more purposeful. We do this by leveraging state-of-the-art technologies, modern architecture, and industry experts to create AI-powered solutions that transform the way our clients do business. The new AI Transformation team will build on Crowe’s established AI foundation, furthering the capabilities of our Applied AI / Machine Learning team. By combining Generative AI, Machine Learning and Software Engineering, this team empowers Crowe clients to transform their business models through AI, irrespective of their current AI adoption stage. As a member of AI Transformation, 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. About the Team We invest in expertise. You’ll have the time, space, and support to go deep in your projects and build lasting technical and strategic mastery. You’ll work with developers, product stakeholders, and project managers as a trusted leader 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. About the Role Senior AI Engineer I (Senior Staff) leads the design, development, and optimization of advanced AI and machine learning systems with a high degree of autonomy. This role partners closely with architects, product leaders, and engineering teams to deliver end-to-end, cloud-native AI solutions that are scalable, secure, and production-ready. As a senior staff-level individual contributor, the engineer drives technical decision-making across data, model, and infrastructure layers, resolves complex engineering challenges, and establishes technical standards that elevate team-wide engineering practices. The role combines deep hands-on expertise with mentorship, cross-team collaboration, and strategic input into roadmap planning. This position plays a key role in advancing enterprise AI and generative AI capabilities while ensuring alignment with security, compliance, data governance, and responsible AI standards.

Requirements

4+ years of professional experience in AI/ML engineering or software engineering.
Demonstrated experience leading technical efforts or mentoring engineers.
Deep proficiency in Python, modern ML frameworks, and cloud-native engineering practices.
Strong understanding of distributed systems, data pipelines, and model optimization techniques.
Proven ability to lead technical designs and perform advanced debugging across complex systems.
Effective communication skills for cross-functional technical alignment.
Willingness to travel occasionally for cross-functional planning and collaboration.

Nice To Haves

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent experience.
Master’s degree or equivalent advanced study in a relevant technical discipline.
Cloud and AI certifications, including Azure (AZ-900, AI-900, AI-102, GH-900, DP/AB series) or equivalent AWS/GCP certifications.
Databricks Generative AI Associate certification.
Advanced hands-on experience with AWS, Azure, or Google Cloud.
Strong experience with containerization (Docker), Kubernetes (EKS, AKS, GKE), and infrastructure automation (Terraform).
Experience with cloud ML platforms such as SageMaker, Vertex AI, or Azure ML.
Hands-on experience with GPU and accelerator-based workloads and distributed training jobs.
Strong knowledge of observability, monitoring, and performance tuning for AI systems.
Advanced generative AI expertise, including fine-tuning transformer models using PyTorch and/or TensorFlow.
Experience designing and deploying RAG systems using vector databases (e.g., Pinecone, Weaviate, FAISS).
Experience building GenAI services with frameworks such as LangChain or LlamaIndex.
Experience integrating and evaluating LLM APIs (e.g., OpenAI, Azure OpenAI, Gemini).
Familiarity with parameter-efficient fine-tuning techniques (PEFT, LoRA, QLoRA).
Experience designing evaluation frameworks for LLM quality, safety, bias, latency, and scalability.
Demonstrated ability to work independently and solve complex, ambiguous problems.

Responsibilities

Designing and implementing complex AI/ML systems, pipelines, and model-serving architectures for enterprise workloads.
Developing reusable frameworks, libraries, and tools to accelerate AI engineering across teams.
Analyzing large-scale datasets, model telemetry, and inference performance to drive optimization strategies.
Architecting distributed training, evaluation, and experimentation workflows to improve model reliability and accuracy.
Collaborating with senior stakeholders to define technical requirements, solution approaches, and feasibility assessments.
Providing hands-on technical leadership through design reviews, code reviews, and mentorship of junior and mid-level engineers.
Implementing advanced automated testing, including stress testing, bias detection, non-regression testing, and quality validation.
Troubleshooting complex pipeline failures, infrastructure issues, and distributed system bottlenecks.
Optimizing performance across the full model lifecycle, including data preprocessing, training, and inference.
Documenting architectural decisions, engineering patterns, and best practices to strengthen organizational knowledge.
Contributing expert-level recommendations to AI roadmap planning and future capability development.
Participating in incident response and operational support for deployed AI systems.
Researching and evaluating emerging AI, generative AI, and cloud technologies for enterprise applicability.