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.

- Robust 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.

📌 Ai Ml Architect (Noida)
🏢 Birch Tree Consulting
📍 Noida