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Machine Learning Solutions Architect

phData • United States
Full Remote
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Design and lead production-ready Applied AI solutions for clients, translating business challenges into scalable ML/MLOps architectures. Collaborate with stakeholders to implement generative AI, LLMs, and intelligent automation. Requires 8+ years in AI/ML production systems with hands-on cloud/data expertise.

Key Highlights
Lead end-to-end Applied AI architecture design for predictive ML, MLOps, generative AI, and agentic workflows
Translate business requirements into production-ready AI solutions with measurable outcomes
Collaborate with global stakeholders to guide AI initiatives from experimentation to production
Key Responsibilities
Design and implement production-ready AI systems for clients
Translate business challenges into technical AI/ML architectures
Collaborate with stakeholders to guide AI initiatives from prototype to production
Ensure AI systems meet security, observability, governance, and scalability standards
Technical Skills Required
Machine Learning/MLOps Cloud Platforms (AWS/Azure/GCP) Python & SQL
Benefits & Perks
Remote-first work environment
401k plan with company match
Dental and vision insurance
Home office equipment stipend
Annual learning and development stipend
4 weeks PTO plus 10 holidays
Nice to Have
Experience with agentic AI systems and RAG architectures
Cloud-native AI/ML service expertise (AWS Bedrock, Azure AI/ML, etc.)
Model monitoring, drift detection, and AI evaluation experience

Job Description

We are looking for a Machine Learning Solutions Architect to join our Applied AI team.

In this role, you will translate complex business challenges into practical, production-ready Applied AI solutions that deliver measurable value for our clients. You will collaborate closely with client stakeholders, senior architects, and cross-functional delivery teams to design and guide implementation of robust AI systems. You will help move AI initiatives from experimentation to production while ensuring reliability, scalability, governance, and responsible AI adoption.

Key Responsibilities

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