Ai Ml Architect

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.