Applied AI Solutions Architect

About phData

phData is a data and AI services company that helps enterprises turn data and AI ambition into measurable business impact. Our Applied AI practice helps clients move from experimentation to production across predictive machine learning, MLOps, generative AI, LLM applications, agentic workflows, and intelligent automation.

We build intelligent systems on modern data and cloud foundations, connecting enterprise data, knowledge, and processes so people and AI applications can make better decisions and take meaningful action.

About the Role

The Applied AI Solutions Architect is a client-facing technical role within phData’s Applied AI practice. You’ll help customers translate business challenges into practical, production-ready solutions across machine learning, generative AI, AI applications, data platforms, and cloud infrastructure.

You’ll work with client stakeholders, project teams, and senior architects to understand requirements, design solution approaches, and guide implementation. You’ll contribute across the full engagement lifecycle—from discovery and architecture through development, deployment, and production support.

This role is ideal for a technically strong consultant who enjoys solving ambiguous problems, working directly with customers, and connecting architecture decisions to measurable business outcomes. You’ll be expected to lead technical workstreams, contribute hands-on when needed, and grow your impact through collaboration, delivery excellence, and continuous learning.

What You’ll Do

Applied AI Solution Architecture

• Design practical Applied AI solutions aligned to customer objectives, success criteria, and technical requirements.
• Contribute to architectures spanning predictive machine learning, MLOps, generative AI, LLM applications, agentic workflows, and intelligent automation.
• Translate business problems into solution designs, technical requirements, implementation plans, and measurable outcomes.
• Design AI application architectures using patterns such as Retrieval-Augmented Generation (RAG), semantic retrieval, tool and function calling, orchestration, and human-in-the-loop workflows.
• Evaluate technology choices and make recommendations across cloud platforms, data platforms, AI services, frameworks, and enterprise systems.
• Document architecture decisions, system interactions, tradeoffs, risks, and implementation considerations.
• Design solutions with appropriate attention to reliability, scalability, security, observability, evaluation, governance, and responsible AI adoption.
• Partner with senior architects and technical leads to review designs and resolve complex technical challenges.

Client Discovery and Consulting

• Participate in discovery sessions, architecture workshops, technical demonstrations, and roadmap discussions.
• Work directly with business and technical stakeholders to understand their goals, workflows, data, systems, and constraints.
• Ask effective questions and translate ambiguous requirements into structured use cases and solution options.
• Explain technical concepts clearly to both technical and non-technical audiences.
• Help clients evaluate Applied AI opportunities based on business impact, feasibility, readiness, and responsible implementation.
• Build trusted client relationships through strong communication, ownership, and follow-through.

Delivery and Technical Leadership

• Lead technical workstreams and contribute to the successful delivery of Applied AI engagements.
• Translate solution designs into implementation tasks, technical priorities, and delivery milestones.
• Collaborate with data scientists, machine learning engineers, software engineers, data engineers, platform engineers, and business stakeholders.
• Contribute hands-on to prototypes, proofs of concept, production implementations, integrations, and technical deliverables.
• Review designs, code, documentation, testing, and production-readiness criteria.
• Help teams navigate technical challenges, changing requirements, delivery risks, and stakeholder expectations.
• Communicate technical decisions, risks, progress, and tradeoffs clearly to customers and internal teams.
• Ensure solutions are delivered with high quality, within scope, and with measurable business value.

Production AI and Operationalization

• Help move AI and machine learning solutions from experimentation into reliable production systems.
• Implement or guide patterns for model deployment, inference, monitoring, evaluation, retraining, and ongoing operations.
• Contribute to model registries, feature stores, data and model lineage, drift detection, performance monitoring, and CI/CD practices.
• Help design evaluation suites, guardrails, observability, prompt management, and human review for generative and agentic systems.
• Support secure, governed, cost-conscious, and responsible AI implementations.
• Help integrate AI solutions with enterprise data, applications, workflows, and operating processes.

Solution Shaping and Sales Support

• Support Sales and senior architects with technical discovery, solution shaping, and proposal development.
• Contribute to technical proposals, statements of work, reference architectures, demonstrations, workshops, and proofs of concept.
• Help translate customer needs into solution approaches, delivery plans, timelines, estimates, and technical requirements.
• Identify opportunities to combine predictive ML, generative AI, agentic capabilities, data engineering, and platform services to solve customer challenges.
• Communicate phData’s technical capabilities and recommended solutions with clarity and credibility.

Practice Contribution and Collaboration

• Contribute to reusable reference architectures, accelerators, frameworks, templates, playbooks, and delivery standards.
• Share lessons learned and best practices that improve delivery quality and customer outcomes.
• Mentor and support less-experienced engineers, consultants, and project team members.
• Participate in internal technical communities, enablement sessions, customer workshops, and practice initiatives.
• Stay current with advances in AI, machine learning, data, cloud, software engineering, and AI-assisted development.
• Help translate relevant technologies and patterns into practical guidance for clients and colleagues.

What We’re Looking For

Qualifications

• 8+ years of experience designing, building, or delivering Data, Analytics, Cloud, Software, Machine Learning, or AI solutions.
• At least 5 years of experience designing or leading AI, machine learning, MLOps, or data-intensive solutions in production.
• Experience translating business and technical requirements into architecture, solution designs, and implementation plans.
• Strong understanding of Applied AI and modern machine learning systems, including predictive ML, MLOps, generative AI, LLM applications, RAG, or agentic architectures.
• Hands-on experience with production AI/ML systems, including considerations for evaluation, observability, security, governance, and operational support.
• Experience with modern cloud, data, and AI ecosystems, including technologies such as Snowflake, Databricks, AWS, Azure, Google Cloud, dbt, Anthropic, OpenAI, or other modern AI frameworks.
• Experience with programming and software development, preferably Python, as well as strong working knowledge of SQL.
• Ability to collaborate effectively with business stakeholders and highly technical engineering teams.
• Strong written and verbal communication, presentation, facilitation, and problem-solving skills.
• Experience working in a consulting, professional services, product, or client-facing delivery environment.
• Demonstrated ability to take ownership, manage multiple priorities, and deliver high-quality work with appropriate guidance.
• Willingness to travel as needed to support customers, workshops, and strategic engagements.

Preferred Qualifications

• Experience with agentic AI systems, including orchestration, tool use, planning, memory, multi-agent patterns, or protocols such as MCP.
• Experience designing and deploying enterprise RAG systems, semantic retrieval, AI applications, or intelligent workflow automation.
• Experience with cloud-native AI/ML services such as AWS Bedrock and SageMaker, Azure AI/ML, Google Vertex AI, or Snowflake Cortex.
• Experience with model registries, feature stores, data and model lineage, model monitoring, drift detection, AI evaluations, prompt management, or AI gateways.
• Experience building reusable accelerators, modular architectures, or solutions deployed across multiple clients.
• Industry experience building AI or machine learning solutions in life sciences, retail, financial services, manufacturing, or other enterprise environments.
• Experience presenting technical solutions to customers or contributing to proposals, RFIs, RFPs, workshops, or demonstrations.
• Contributions to open-source projects, technical communities, blogs, podcasts, or other technical thought leadership.
• Bachelor’s or master’s degree in computer science, engineering, data science, or a related technical field—or equivalent practical experience.

Why phData?

• Impactful work with leading organizations on meaningful Data and Applied AI initiatives.
• A collaborative, global team that values transparency, autonomy, curiosity, ownership, and continuous improvement.
• Opportunities to work across predictive machine learning, MLOps, generative AI, agentic systems, and intelligent applications.
• Access to challenging projects, mentorship, technical communities, and ongoing professional development.
• A culture focused on psychological safety, community, and doing the right thing for our clients, teams, and partners.