We are seeking a Solutions Architect to help design and deliver the technical foundations that power AI enabled products across the Kroger Precision Marketing portfolio. In this role, you will define practical architecture patterns that make data, APIs, and platform capabilities easier to access, govern, integrate, and scale.
You will work closely with data scientists, ML engineers, software engineers, data engineers, product managers, and business stakeholders to translate complex product and data needs into production ready solutions. This includes shaping architecture, validating ideas through prototypes and proofs of concept, and helping teams move from early concepts to scalable implementations that serve clients, internal users, applications, and AI agents.
This role is ideal for someone who enjoys both architecture and execution. Someone who can facilitate a future state design conversation, define an API integration pattern, evaluate an AI use case, and then help a team create a working prototype or reference implementation.
What You’ll Do
- Partner with product managers, engineers, data scientists, machine learning engineers, and business stakeholders to understand problems, shape solutions, and define technical paths forward.
- Design end to end solution architectures across data platforms, APIs, AI enabled applications, internal tools, and client facing capabilities.
- Translate business and product needs into technical designs, implementation plans, architecture diagrams, and phased delivery roadmaps.
- Build prototypes, proofs of concept, and reference implementations to validate technical feasibility and help teams move faster.
- Help define practical architecture patterns for cloud applications, APIs, data pipelines, semantic layers, AI and machine learning workflows, and event driven integrations.
- Work with engineering teams to make key technical decisions around system design, integration patterns, data models, platform choices, and build versus buy tradeoffs.
- Help shape API strategies that make data, models, and platform capabilities easier to consume securely and consistently across teams and products.
- Create solution design documents, API specifications, architecture decision records, reference architectures, and technical implementation guidance.
- Embed with delivery teams when needed to clarify requirements, unblock implementation issues, validate technical feasibility, or help move a concept from prototype to production.
- Partner with security, data governance, and platform teams to ensure solutions are secure, compliant, observable, and supportable.
- Mentor engineers and technical teams on architecture practices, API design, data architecture, AI system design, and pragmatic delivery.
- Evaluate emerging technologies, including managed AI platforms, semantic layer tools, agent frameworks, and cloud data services, with a focus on practical adoption.
Qualifications, Skills, Experience
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field, or equivalent practical experience.
- 7 or more years of experience in software engineering, data engineering, platform engineering, solution architecture, or a related technical role.
- 3 or more years of experience in a technical leadership, architecture, staff engineer, principal engineer, or solution design role.
- Experience designing distributed systems, APIs, data platforms, or cloud applications.
- Strong understanding of architecture patterns such as microservices, API first design, event driven architecture, domain driven design, and evolutionary architecture.
- Experience with cloud platforms, preferably Azure, and modern data platform patterns.
- Experience building prototypes or proof of concept solutions to validate ideas before full scale implementation.
- Experience working with cross functional teams including product, engineering, data science stakeholders.
- Experience with CI/CD, automated testing, monitoring, logging, and production support practices.
- Experience with Databricks, Snowflake, Delta Lake, Azure Data Lake Storage, Spark, or similar cloud data platforms.
- Familiarity with AI and machine learning system patterns such as model training pipelines, inference services, feature engineering, model registries, or MLOps.
- Familiarity with LLM application patterns such as retrieval augmented generation, semantic search, vector databases, prompt orchestration, or AI agents.
- Experience with managed AI services such as Azure OpenAI, Databricks AI and ML capabilities, Snowflake Cortex AI, or similar platforms.
- Experience designing REST, GraphQL, event based, or asynchronous APIs.
- Familiarity with API gateways, OAuth, OIDC, OpenAPI specifications, rate limiting, versioning, and service to service authentication.
- Experience with governance, security, and observability concepts including RBAC, lineage, audit logging, cost attribution, data access controls, and platform monitoring.
- Experience in retail media, ad tech, marketing technology, customer analytics, loyalty, or large scale consumer data environments.
