AI/ML Architect - Talent Pipeline

Job description

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 - Architect and deliver end-to-end AI/ML and Generative AI solutions from discovery through production deployment. - Design production-grade RAG systems, vector search solutions, embedding pipelines, AI agents, copilots, and multi-agent workflows. - Lead hands-on implementation using Snowflake Cortex, Cortex Analyst, Cortex Search, Cortex Agents, Snowpark ML, and related Snowflake AI capabilities. - Design scalable ML and LLM pipelines supporting training, inference, evaluation, monitoring, and governance. - Integrate AI solutions with AWS, Azure, or GCP, as well as enterprise applications, APIs, ETL/ELT platforms, streaming systems, and third-party AI services. - Define reusable AI reference architectures, accelerators, technical standards, and governance frameworks. - Provide technical leadership through architecture reviews, code reviews, design sessions, and production-readiness assessments. - Lead client discovery workshops, solution design, effort estimation, technical presentations, proof-of-concepts, and pre-sales activities. - Collaborate with data engineers, ML engineers, application teams, architects, and business stakeholders throughout delivery. ## Required Qualifications - 10+ years of experience in solution architecture, AI/ML architecture, enterprise architecture, or technical delivery. - Strong recent hands-on experience building and supporting production AI/ML and Generative AI solutions. - Proven experience with LLMs, RAG, vector databases, embedding pipelines, agentic AI, prompt engineering, and AI evaluation. - Strong understanding of machine learning, deep learning, NLP, MLOps, and LLMOps. - Hands-on experience with Snowflake Cortex, Cortex Analyst, Cortex Search, Cortex Agents, Snowpark ML, and Snowflake Native Apps. - Strong architecture experience across Snowflake and at least one major cloud platform, preferably AWS or Azure. - Experience integrating AI platforms with APIs, microservices, SaaS applications, enterprise systems, Kafka, and event-driven architectures. - Strong understanding of AI security, governance, compliance, observability, and model monitoring. - Excellent client-facing communication, technical leadership, and stakeholder-management skills. - Ability to balance strategic architecture ownership with hands-on technical execution. ## Preferred Qualifications - Experience with Amazon Bedrock, SageMaker, Azure OpenAI, Azure AI Foundry, Azure ML, or Vertex AI. - Familiarity with LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or MCP. - Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, Chroma, or pgvector. - Experience integrating AI solutions with platforms such as Salesforce, ServiceNow, SAP, Workday, or Microsoft applications. - Snowflake, AWS, Azure, or AI/ML certifications. - Consulting experience within regulated industries such as financial services, healthcare, or manufacturing.