Job description

Senior AI Solutions Architect Dynata is seeking a Senior AI Solutions Architect to lead the design of the foundational architectures that power the company’s next-generation AI, machine learning, and LLM-enabled data ecosystem. This Senior AI Architect will define the technical patterns, governance frameworks, and integration standards that connect Dynata’s Lakehouse, feature ecosystem, retrieval pipelines, and AI/ML capabilities into a scalable and reusable platform. Working at the intersection of AI systems architecture, data engineering, governance, and enterprise enablement, this role will ensure that data and knowledge assets are optimized for traditional ML, embeddings, LLMs, agentic workflows, and emerging AI applications. Reporting to the VP, Data Science, this role will partner closely with product, engineering, and platform teams to establish the architectural standards that support Dynata’s evolving portfolio of AI-driven products, intelligent workflows, and enterprise decision-support systems. Key Responsibilities Translate business concepts, governance standards, and domain definitions into scalable technical frameworks and enforceable architectures. • Translate complex technical concepts into clear architectural decisions and implementation guidance.  • Establish standards for how analytical models, machine learning solutions, and AI services integrate with enterprise data assets.  Design and personally build pipelines end-to-end, from ingestion through retrieval and grounding logic for proof of concepts Design and write the orchestration code for AI workflows, including tool integration via protocols such as MCP, state management, and failure handling. Define architectural patterns for feature stores, model inputs and outputs Partner closely with Product, Technology, Research & Data Science, and Data Platform teams.  Implement embedding and metadata-tagging code against agreed taxonomies, working directly from domain experts input rather than handing off a spec. Build data-quality and AI guardrails Build metadata and lineage capture into pipelines so governance is enforced by code Qualifications 7 years hands-on software or ML engineering experience Familiarly with Databricks and MLFlow. Able to translate ambiguous requirements into a working prototype quickly Strong coding ability in a modern data/ML stack Experience writing feature pipelines and model-serving or integration code. Comfortable with modern engineering workflows: version control, code review, CI/CD, and testing discipline for data and ML pipelines with and without AI tools (Claude, Copilot, Cursor, etc) Experience implementing schema versioning, data-quality checks, and lineage capture as code. Strong communication skills for explaining technical trade-offs to engineers and executive stakeholders, but the day-to-day is engineering. Preferred Qualifications Comfortable with AI assisted development processes Direct implementation experience building or integrating MCP servers/clients, or other agent-to-tool protocols Experience writing semantic-layer or metadata code that other pipelines consume. Background building forecasting, optimization, or graph-analytics systems undefined At Dynata, we deliver the highest quality first-party data to help businesses around the world gain precise insights, activate the right audiences, and confidently measure impact. With industry-leading respondent accuracy, reliability, and a commitment to continuous improvement, Dynata is the trusted foundation for smarter decision-making. At Dynata, we are committed to creating an inclusive and accessible environment where every employee and customer feels valued, respected, and supported. We strive to build a workforce that reflects the diversity of the communities we serve. Dynata welcomes and encourages applications from individuals with disabilities and is dedicated to fostering a work culture that supports everyone. Accommodations are available upon request for all aspects of the selection process. Dynata is an Equal Opportunity Employer. We consider all qualified applicants and employees without regard to race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, marital status, age, disability, genetic information, veteran status, or any other legally protected status under applicable laws. T he base salary range for this position in is $120K-$150K/yr; however, base pay offered may vary depending on location, job-related knowledge, skills, and experience. A discretionary incentive program may be provided as part of the compensation package, in addition to a full range of medical and other benefits, dependent on full-time employment status. #LI-Remote

Requirements

Must-haves

Senior AI Solutions Architect
Dynata is seeking a Senior AI Solutions Architect to lead the design of the foundational architectures that power the company’s next-generation AI, machine learning, and LLM-enabled data ecosystem.
Translate business concepts, governance standards, and domain definitions into scalable technical frameworks and enforceable architectures.
Translate complex technical concepts into clear architectural decisions and implementation guidance.
Establish standards for how analytical models, machine learning solutions, and AI services integrate with enterprise data assets.
Design and personally build pipelines end-to-end, from ingestion through retrieval and grounding logic for proof of concepts
Design and write the orchestration code for AI workflows, including tool integration via protocols such as MCP, state management, and failure handling.
Define architectural patterns for feature stores, model inputs and outputs
Partner closely with Product, Technology, Research & Data Science, and Data Platform teams.
Implement embedding and metadata-tagging code against agreed taxonomies, working directly from domain experts input rather than handing off a spec.
Build data-quality and AI guardrails
Build metadata and lineage capture into pipelines so governance is enforced by code
7 years hands-on software or ML engineering experience
Familiarly with Databricks and MLFlow.
Able to translate ambiguous requirements into a working prototype quickly
Strong coding ability in a modern data/ML stack
Experience writing feature pipelines and model-serving or integration code.
Comfortable with modern engineering workflows: version control, code review, CI/CD, and testing discipline for data and ML pipelines with and without AI tools (Claude, Copilot, Cursor, etc)
Experience implementing schema versioning, data-quality checks, and lineage capture as code.
Strong communication skills for explaining technical trade-offs to engineers and executive stakeholders, but the day-to-day is engineering.

Nice-to-haves

Comfortable with AI assisted development processes
Direct implementation experience building or integrating MCP servers/clients, or other agent-to-tool protocols
Experience writing semantic-layer or metadata code that other pipelines consume.
Background building forecasting, optimization, or graph-analytics systems

Application prep

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

Keywords to mirror

aimachine learningllmmlci/cd

Platform: workday
Effort: long