Role Description You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities - Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. - Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. - Working directly with product teams and ML engineers to improve their day-to-day productivity. - Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies. Qualifications - 2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems. - Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations. - Experience working on production ML platforms, MLOps solutions, or building LLM applications. - Experience running operations for high availability, low latency systems. - Experience partnering with other teams to drive business outcomes. - A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course. Preferred Qualifications - Experience building and shipping production AI agents. - Familiarity with the LLMs and LLM Frameworks. - Experience training and shipping machine learning models to production to solve critical business problems.

• Own problems end-to-end from exploratory R&D through production-hardened, maintainable systems • Develop and extend GPU-accelerated code in PyTorch and CUDA C++ • Work across a broad modeling landscape including RL, graph neural networks, black-box/classical optimization, and generative modeling • Formulate objectives, model constraints, and debug numerical behavior in the stack • Contribute to technical direction and research strategy alongside senior teammates

Role Description MicroTech is seeking a Director of Artificial Intelligence and Machine Learning to lead and grow our AI capability across the business and for our customers. This is a leadership position for someone who understands both the business and technical sides of AI/ML and can turn emerging technologies into practical, scalable, and marketable solutions. - Assess current capabilities and strengthen the existing team. - Define a clear roadmap for AI/ML initiatives. - Build solutions that improve internal efficiency and address customer challenges. - Evaluate and leverage commercial off-the-shelf AI/ML tools. - Guide the company in building user-friendly and relevant AI solutions. Qualifications - Bachelor's degree in computer science, engineering, data science, information systems, business, or a related field; advanced degree preferred. - 10+ years of progressive experience in technology, software, data, AI/ML, automation, or digital transformation roles. - 5+ years of leadership experience with responsibility for strategy, execution, and team development. - Strong understanding of AI/ML technologies, including large language models, machine learning, NLP, retrieval/search, intelligent automation, and model deployment. - Experience evaluating, implementing, and integrating commercial AI/ML platforms and tools. - Demonstrated ability to translate technical capabilities into business solutions, products, or customer offerings. - Experience leading cross-functional teams and working effectively with executives, engineers, operations personnel, and business stakeholders. - Strong communication skills and the ability to explain AI/ML concepts in practical business terms. - Experience in a small business or growth-oriented environment where speed, flexibility, and practical execution matter. Requirements - Experience supporting Federal, Defense, or other regulated-industry customers. - Familiarity with secure on-prem, hybrid cloud, or private cloud AI environments. - Experience with knowledge management, document intelligence, enterprise search, or workflow automation solutions. - Background in productization, managed services, or recurring service delivery models. - Experience developing AI solutions that move beyond prototypes into real operational use. Benefits - Insurance (medical, dental, vision). - Paid Time Off (PTO): 15 days during the first year; 20 days annually after one year of service. - 401k Plan with Employer Matching Contribution. - 10 Company-Paid Holidays. - Tuition Assistance. - Voluntary Benefit Programs. - Corporate Discounts. Company Description MicroTech is an award-winning Service-Disabled Veteran-Owned Small Business (SDVOSB) and experienced provider of information technology and communications. We offer a wide range of professional services focused on providing cutting-edge solutions with the customer at the forefront of every decision we make. - Solving complex business challenges is our passion. - We provide effective, practical solutions that can increase productivity and decrease costs. - MicroTech has developed a well-earned reputation for best-in-class services and solutions. - We are committed to people development and providing high achievers opportunities to grow professionally.

• Design, build, and maintain scalable MLOps pipelines for model training, validation, deployment, and monitoring using AWS services • Implement infrastructure as code and CI/CD workflows to support rapid experimentation and reliable production releases • Collaborate with data scientists to productionize ML models and ensure reproducibility, versioning, and traceability • Monitor model performance and data drift in production environments, and implement automated retraining and alerting mechanisms • Optimize ML workflows using tools such as SageMaker, Airflow, Docker, Kubernetes (EKS), and Step Functions • Ensure compliance with healthcare data standards and security best practices (e.g., HIPAA) • Contribute to the continuous improvement of MLOps practices and advocate for automation and scalability across the ML lifecycle