AI/MLOps Architect

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
• 8+ years of experience designing and implementing enterprise data, analytics, AI, or cloud solutions.
• 3+ years of hands-on experience with MLOps, machine learning platform engineering, or AI platform architecture.
• Experience architecting and operationalizing machine learning solutions in enterprise environments.
• Strong experience engaging business stakeholders and translating business requirements into scalable technical solutions.
• Proven ability to lead technical evaluations, platform selections, and proof-of-concept initiatives.
• Experience with at least one major cloud platform: AWS, Azure, or Google Cloud Platform.
• Strong understanding of CI/CD, Infrastructure as Code, containerization, and cloud-native architecture patterns.
• Experience with Kubernetes, Docker, GitOps, and modern DevOps practices.
• Knowledge of model lifecycle management, ML governance, monitoring, and model performance management.

Preferred Qualifications

• Deep expertise within the Snowflake ecosystem, including Snowpark, Cortex AI, Native Apps, Container Services, and ML capabilities.
• Experience with leading MLOps and ML platform solutions such as Databricks, Azure ML, Vertex AI, SageMaker, Dataiku, Domino, DataRobot, Kubeflow, MLflow, or similar platforms.
• Experience supporting Generative AI, LLMOps, Retrieval Augmented Generation (RAG), vector databases, and AI agent architectures.
• Knowledge of responsible AI, model governance, regulatory compliance, and enterprise security frameworks.
• Familiarity with data engineering and modern data platform architectures.
• Snowflake, cloud platform, or AI/ML-related certifications are highly desirable.

 Perficient, Inc. proudly provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, genetic information, marital status, amnesty, or status as a protected veteran in accordance with applicable federal, state and local laws. Perficient, Inc. complies with applicable state and local laws governing non-discrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. Perficient, Inc. expressly prohibits any form of unlawful employee harassment based on race, color, religion, gender, sexual orientation, national origin, age, genetic information, disability, or covered veterans. Improper interference with the ability of Perficient, Inc. employees to perform their expected job duties is absolutely not tolerated.

• Lead discovery sessions with business and technology stakeholders to identify AI/ML platform requirements, use cases, and strategic objectives.
• Evaluate, design, and onboard enterprise AI/ML and MLOps platforms to support scalable machine learning development, deployment, governance, and monitoring.
• Architect and deliver rapid end-to-end proof-of-concepts demonstrating business value, technical feasibility, and operational readiness.
• Provide thought leadership and technical guidance around the Snowflake ecosystem, including Snowpark, Cortex AI capabilities, ML workflows, data governance, and data sharing strategies.
• Assess and compare competitive MLOps platforms and tools, including cloud-native and third-party offerings, providing recommendations aligned with enterprise goals.
• Define reference architectures, implementation patterns, security frameworks, and operational best practices for AI/ML solutions.
• Partner with data scientists, data engineers, platform teams, and solution architects to establish repeatable MLOps processes and delivery frameworks.
• Design CI/CD and ML lifecycle automation strategies covering model training, validation, deployment, monitoring, retraining, and governance.
• Establish standards for model observability, explainability, lineage, compliance, and responsible AI practices.
• Evaluate platform capabilities across areas such as feature stores, model registries, experiment tracking, orchestration, vector databases, LLMOps, and AI governance.
• Support client pursuits, solution workshops, and pre-sales activities by developing demonstrations, architectural recommendations, and implementation roadmaps.
• Stay current with emerging AI, GenAI, LLMOps, and MLOps technologies and translate industry trends into actionable recommendations for clients