Applied AI Solutions Architect
RemoteUnited States
Job Summary
Lead the design of end-to-end Applied AI architectures spanning predictive ML, MLOps, generative AI, LLM applications, and agentic workflows aligned to client objectives. Translate ambiguous business requirements into clear solution designs, technical requirements, implementation roadmaps, and measurable success criteria for client engagements. Guide and contribute hands-on to prototypes, proofs of concept, and production implementations, ensuring high-quality delivery and smooth transitions into reliable operations. Partner with client and internal stakeholders through discovery sessions and workshops to evaluate AI opportunities, recommend technology choices, and de-risk complex technical decisions. Ensure production systems are designed with strong attention to security, observability, evaluation, governance, cost-effectiveness, and ongoing monitoring.
Required Qualifications
- 8+ years of experience designing, building, or delivering Data, Analytics, Cloud, Software, Machine Learning, or AI solutions
- at least 5 years designing or leading AI, machine learning, MLOps, or data-intensive solutions in production
- Strong understanding of Applied AI and modern machine learning systems, including predictive ML, MLOps, generative AI, LLM applications, RAG, and agentic architectures
- Hands-on experience with production AI/ML systems, including evaluation, observability, security, governance, and operational support
- Experience with modern cloud, data, and AI ecosystems such as Snowflake, Databricks, AWS, Azure, Google Cloud, dbt, Anthropic, OpenAI, or similar frameworks
- Proficiency with programming and software development, preferably in Python
- strong working knowledge of SQL
- Proven ability to translate business and technical requirements into architectures, solution designs, and actionable implementation plans
- Bachelor's or master's degree in computer science, engineering, data science, or a related technical field, or equivalent practical experience
Desired 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, and contributing to proposals, workshops, demonstrations, or broader technical communities
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