Machine Learning Solutions Architect
New
P
phDataData and AI
Location: US - RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience designing, building, or delivering Data, Analytics, Cloud, Software, Machine Learning, or AI solutions, including at least 5 years designing or leading AI, machine learning, MLOps, or data-intensive solutions in production.
- Required Skills
- PythonSQLMachine LearningSnowflakeMLOpsGenerative AI
Requirements
- Have 8+ years of experience designing, building, or delivering Data, Analytics, Cloud, Software, Machine Learning, or AI solutions.
- Have at least 5 years designing or leading AI, machine learning, MLOps, or data-intensive solutions in production.
- Have a strong understanding of Applied AI and modern machine learning systems, including predictive ML, MLOps, generative AI, LLM applications, RAG, and agentic architectures.
- Have hands-on experience with production AI/ML systems, including evaluation, observability, security, governance, and operational support.
- Have experience with modern cloud, data, and AI ecosystems such as Snowflake, Databricks, AWS, Azure, Google Cloud, dbt, Anthropic, or OpenAI.
- Have programming and software development proficiency, preferably in Python, and strong working knowledge of SQL.
- Be able to translate business and technical requirements into architectures, solution designs, and actionable implementation plans.
- Be willing to travel up to 40%, depending on client needs.
- A bachelor's or master's degree in computer science, engineering, data science, or a related technical field, or equivalent practical experience, is listed as optional.
Responsibilities
- Lead the design of end-to-end Applied AI architectures spanning predictive ML, MLOps, generative AI, LLM applications, agentic workflows, and intelligent automation.
- Translate ambiguous business requirements into solution designs, technical requirements, implementation roadmaps, and measurable success criteria.
- Guide and contribute hands-on to prototypes, proofs of concept, and production implementations.
- Partner with client and internal stakeholders through discovery sessions, architecture workshops, and roadmap discussions.
- Evaluate AI opportunities, recommend technology choices, and de-risk complex technical decisions.
- Ensure production AI systems address security, observability, evaluation, governance, cost-effectiveness, monitoring, retraining, and support.
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