MLOps Architect

Perficient•United States,

9d•$92,118 - $202,730

About The Position

The MLOps Architect works at the intersection of business strategy, emerging technology, and delivery execution. The role translates enterprise goals into actionable AI initiatives, frames problems, prioritizes opportunities, and ensures alignment with strategic objectives and regulatory environments. It supports scalable, outcome-driven experimentation and delivery of AI solutions across business domains, guiding prototypes and pilots from intake through operationalization. The position is part of Perficient’s Data & Analytics team, which supports clients throughout the data lifecycle and works across AI, data management, content, and data strategy. The architect partners with clients and internal teams to assess, design, and deliver enterprise AI/ML and MLOps platforms and solutions.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
At least 8 years of experience designing and implementing enterprise data, analytics, AI, or cloud solutions.
At least 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.

Nice To Haves

Deep expertise in the Snowflake ecosystem, including Snowpark, Cortex AI, Native Apps, Container Services, and ML capabilities.
Experience with 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.

Responsibilities

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 for scalable machine learning development, deployment, governance, and monitoring.
Architect and deliver rapid end-to-end proof-of-concepts that demonstrate business value, technical feasibility, and operational readiness.
Provide technical guidance on the Snowflake ecosystem, including Snowpark, Cortex AI capabilities, ML workflows, data governance, and data sharing strategies.
Assess and compare cloud-native and third-party MLOps platforms and tools, and recommend options 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 for model training, validation, deployment, monitoring, retraining, and governance.
Establish standards for model observability, explainability, lineage, compliance, and responsible AI practices.
Evaluate platform capabilities including 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 client recommendations.