AI/ML Architect
Posted 29 Jul 2026
Advertised as “AI/ML Architect - Talent Pipeline”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 10+ yrs
- Management level
- People management
- Employment
- Remote
No salary listed for this role.
BlueCloud is a Snowflake Elite Partner and the 2026 CoCo Catalyst Snowflake Partner of the Year. We help enterprise organizations move from fragmented legacy systems to unified, AI-ready Snowflake platforms — delivering data migration, engineering, governance, BI & analytics, and AI/ML solutions 40–50% faster than traditional approaches.
With 450+ Snowflake consultants, 200+ enterprise transformations under our belt, and a 100% Snowflake focus, we combine advisory-led thinking with AI-powered accelerators to turn months of work into weeks of results. Our clients span Financial Services, Healthcare & Life Sciences, Retail, Manufacturing, Energy, and more — and the outcomes speak for themselves: 97% faster reports, 40% fraud reduction, $1.5M in client savings, and 10× client growth.
We don't just strategize — we execute.
About the Role
BlueCloud is seeking an experienced AI/ML Architect to lead the architecture and delivery of enterprise-scale AI, machine learning, and Generative AI solutions.
This is a hands-on architecture role requiring recent experience designing, developing, integrating, and deploying production-grade AI solutions. You will work directly with enterprise clients to translate complex business needs into scalable, secure, and governed architectures across Snowflake, cloud platforms, data ecosystems, and enterprise applications.
Key Responsibilities
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Architect and deliver end-to-end AI/ML and Generative AI solutions from discovery through production deployment.
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Design production-grade RAG systems, vector search solutions, embedding pipelines, AI agents, copilots, and multi-agent workflows.
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Lead hands-on implementation using Snowflake Cortex, Cortex Analyst, Cortex Search, Cortex Agents, Snowpark ML, and related Snowflake AI capabilities.
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Design scalable ML and LLM pipelines supporting training, inference, evaluation, monitoring, and governance.
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Integrate AI solutions with AWS, Azure, or GCP, as well as enterprise applications, APIs, ETL/ELT platforms, streaming systems, and third-party AI services.
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Define reusable AI reference architectures, accelerators, technical standards, and governance frameworks.
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Provide technical leadership through architecture reviews, code reviews, design sessions, and production-readiness assessments.
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Lead client discovery workshops, solution design, effort estimation, technical presentations, proof-of-concepts, and pre-sales activities.
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Collaborate with data engineers, ML engineers, application teams, architects, and business stakeholders throughout delivery.