MLOps Architect at Perficient

Job description

We are seeking strategic and technically fluent AI Strategists to lead ideation and planning within a structured AI/ML innovation environment. This role is central to enabling scalable, outcome-driven experimentation and delivery of AI solutions across diverse business domains. The AI Strategist works cross-functionally to identify high-impact opportunities, define use cases, and guide prototypes and pilots through the innovation funnel—from intake to operationalization. Job Overview: The AI/MLops architect operates at the intersection of business strategy, emerging technology, and delivery execution. They are responsible for translating enterprise goals into actionable AI initiatives, framing problems, prioritizing opportunities, and ensuring alignment with strategic objectives and regulatory environments. Our Data & Analytics team drives cutting-edge innovation in digital transformation deployment globally and across industries. We partner with a diverse range of clients and support them at every stage of the data lifecycle—from curation and ingestion to management, analytics, and reporting. Our practice spans AI, Data Management, Content, Data Strategy, and more. From building modern data platforms to applying machine learning and generative AI, we help organizations unlock deeper insights, automate decision-making, and drive smarter outcomes. In our collaborative, fast-paced environment, you’ll work with industry-leading tools and technologies, tackling real-world challenges and delivering impactful solutions that shape the future of digital transformation. • 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 • 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. Disability Accommodations: Perficient is committed to providing a barrier-free employment process with reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or accommodation due to a disability, please contact us. Applications will be accepted until the position is filled or the posting is removed. The salary range for this position takes into consideration a variety of factors, including but not limited to skill sets, level of experience, applicable office location, training, licensure and certifications, and other business and organizational needs. The new hire salary range displays the minimum and maximum salary targets for this position across all US locations, and the range has not been adjusted for any specific state differentials. It is not typical for a candidate to be hired at or near the top of the range for their role, and compensation decisions are dependent on the unique facts and circumstances regarding each candidate. A reasonable estimate of the current salary range for this position is $92,118 to $202,730. Please note that the salary range posted reflects the base salary only and does not include benefits or any potential variable compensation. Information regarding the benefits available for this position are in this overview. #LI-PS1 #LI-AI/MLops architect We are seeking strategic and technically fluent AI Strategists to lead ideation and planning within a structured AI innovation environment.

Requirements

Must-haves

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
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningci/cdawsazurekubernetesdocker

Platform: oracle-cloud
Effort: medium