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AI/ML Senior Architect

ClifyX

Cincinnati, OH

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JOB DETAILS
SKILLS
Access Control, Amazon Web Services (AWS), Apache Kafka, Apache Spark, Architectural Services, Artificial Intelligence (AI), Best Practices, Blueprints, Cloud Architecture, Cloud Computing, Communication Skills, Computer Science, Computer Vision, Continuous Deployment/Delivery, Continuous Integration, Cryptography, Data Processing, Data Science, Data Warehousing, Deep Learning, DevOps, Docker, Emerging Technology, Enterprise Architecture, Establish Priorities, Information Technology & Information Systems, Leadership, Machine Learning, Maintain Compliance, Mentoring, Merchandising, Microsoft Windows Azure, Model Validation, Natural Language Processing (NLP), NoSQL, Operational Audit, Performance Analysis, Predictive Modeling, Python Programming/Scripting Language, Regulatory Compliance, Retail Operations, SQL Databases, Supply Chain, Technical Leadership, Technical Strategy, Use Cases
LOCATION
Cincinnati, OH
POSTED
Today
Job Description: AI/ML Senior Architect

Location: Cincinnati, OH (Hybrid - Onsite)

Employment Type: Contract

Duration: 6-12+ Months (Extendable)

Position Summary
Kroger is seeking a highly experienced AI/ML Senior Architect to lead the design, development, and deployment of enterprise-scale Artificial Intelligence and Machine Learning solutions across merchandising, supply chain, customer analytics, and retail operations. This role requires a strategic technology leader with deep expertise in modern AI/ML architectures, MLOps, cloud-native platforms, and Generative AI.
The ideal candidate will work closely with business stakeholders, data scientists, engineers, and enterprise architects to define AI roadmaps and deliver scalable solutions that drive measurable business value.

Key Responsibilities

AI/ML Architecture & Strategy
  • Define end-to-end architecture for AI/ML platforms and applications.
  • Lead the design of scalable, secure, and reusable ML pipelines.
  • Establish enterprise standards, best practices, and governance for AI/ML solutions.
  • Evaluate and recommend emerging technologies, frameworks, and tools.
  • Create architectural blueprints, reference models, and technology roadmaps.
Solution Design & Delivery
  • Architect predictive models, recommendation engines, NLP applications, computer vision, and Generative AI solutions.
  • Design feature stores, model registries, experiment tracking, and inference services.
  • Build batch and real-time scoring solutions.
  • Ensure solutions meet enterprise standards for scalability, security, and compliance.
MLOps & Operationalization
  • Design CI/CD pipelines for model training, validation, deployment, and monitoring.
  • Implement automated retraining, drift detection, and performance monitoring.
  • Establish model versioning, governance, and rollback strategies.
  • Drive adoption of DevOps and MLOps practices across teams.
Cloud & Data Platform Integration
  • Architect solutions on cloud platforms such as Amazon Web Services, Microsoft Azure Machine Learning, or Google Vertex AI.
  • Integrate with data lakes, warehouses, and streaming platforms such as Databricks, Snowflake, Apache Spark, and Apache Kafka.
  • Collaborate with data engineering teams on ingestion and transformation pipelines.
Generative AI & LLM Solutions
  • Design Retrieval-Augmented Generation (RAG) architectures.
  • Build prompt orchestration and agent-based AI solutions.
  • Evaluate and integrate foundation models from providers such as OpenAI, Anthropic, and Meta.
  • Implement vector databases such as Pinecone, Weaviate, and FAISS.
Governance, Security & Responsible AI
  • Ensure adherence to responsible AI principles, explainability, and fairness.
  • Implement data privacy, encryption, and access controls.
  • Establish auditability and compliance with enterprise and regulatory standards.
Leadership & Stakeholder Management
  • Provide technical leadership to architects, data scientists, and engineers.
  • Partner with business leaders to identify and prioritize use cases.
  • Present architectural recommendations to executives and steering committees.
  • Mentor team members and conduct design reviews.
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
  • 15 to 20 years of IT experience with at least 7+ years in AI/ML architecture and solution design.
  • Extensive experience designing and deploying machine learning and deep learning solutions.
  • Strong expertise in Python and libraries such as TensorFlow, PyTorch, and scikit-learn.
  • Hands-on experience with MLOps frameworks including MLflow, Kubeflow, and SageMaker.
  • Experience with containerization and orchestration using Docker and Kubernetes.
  • Strong knowledge of SQL and NoSQL databases.
  • Experience with distributed processing and data engineering concepts.
  • Excellent communication and leadership skills.

About the Company

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ClifyX

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