Data Scientist & AI/ML Architect

Job Title: Data scientist & AI-ML Architect

We are seeking a Data Scientist & AI/ML Architect who will own end-to-end AI and machine learning solution design for our clients. This role requires deep Snowflake-native AI/ML expertise, strong client engagement capability, and the ability to take GenAI and machine learning use cases from initial concept through to production deployment. The Architect will define standards, build working prototypes, and guide delivery teams toward best practices across the full data science lifecycle.

Key Responsibilities

AI/ML Architecture Ownership

• Design end-to-end ML and GenAI architectures on Snowflake's native AI stack.
• Define reference architectures for AI/ML workloads including Cortex AI, Snowpark ML, and ML Functions.
• Establish implementation standards for model development, deployment, and monitoring.

Solution Design & POC Delivery

• Translate client business problems into scalable, Snowflake-native technical designs.
• Build hands-on POCs using Python and SQL to validate feasibility and demonstrate best practices.
• Design ingestion, feature engineering, model serving, and inference layers.

MLOps & Model Lifecycle Management

• Define frameworks for model deployment, versioning, monitoring, and retraining.
• Establish CI/CD practices for ML pipelines on Snowflake.
• Guide best practices for model governance, drift detection, and performance tracking.

Client Advisory & Stakeholder Engagement

• Lead discovery sessions and architecture discussions with business and technical stakeholders.
• Support solution proposals, estimations, and technical presentations.
• Enable clients to independently extend and operate AI/ML solutions post-delivery.

Competitive & Ecosystem Positioning

• Maintain a deep understanding of the AI/ML landscape — including complementary and competing tools.
• Advise clients on where Snowflake's native AI stack fits versus external platforms.
• Stay current on Snowflake product developments in Cortex AI and the broader ML ecosystem.

Technical Leadership

• Mentor engineers and conduct architecture and code reviews.
• Drive internal AI/ML capability development as Claroda scales.
• Promote best practices and a culture of continuous improvement.

Required Qualifications

• 8 years of total experience, with 5+ years in data science, ML engineering, or solutions architecture roles.

• Client-facing or consulting exposure is strongly preferred.

• Strong expertise in:

• Snowflake-native AI/ML — Cortex AI, Snowpark ML, ML Functions

• ML lifecycle — feature engineering, model training, deployment, and monitoring

• MLOps frameworks and production model management

• Strong Python and SQL proficiency.

• Experience with at least one major cloud platform (AWS, Azure, or GCP).

• Ability to present confidently to both technical teams and executive stakeholders.

Good to Have

• Snowflake Certification (SnowPro Advanced or Cortex AI track preferred).
• Experience with LLM orchestration frameworks such as LangChain or LlamaIndex.
• Exposure to vector search, embeddings, and RAG architectures within Snowflake.
• Experience integrating Snowflake with external ML platforms such as SageMaker, AzureML, or Vertex AI.

What We Offer

• Opportunity to work on high-impact AI/ML transformations on the Snowflake platform.
• Fully remote, WFH setup with a culture of ownership, quality, and continuous learning.
• A focused, high-performance environment centered around Snowflake excellence.

Skills: architecture,data science,machine learning,data scientist