Sr. Forward-Deployed AI Engineer, Snowflake

Hello Everyone,

Hope you are doing good!!!!

My name is Pavan and I work with Metasis Information System., I have a great opportunity for you, please find the job details below, if you are interested in applying please send me your updated resume and best time for you to discuss about this opportunity in details.

Title: Sr. Forward-Deployed AI Engineer, Snowflake
Location: Remote
Duration: 3 -6 Months Contract to hire or long-term contract

About Client:
Client is transforming how data works for health plans. Client makes healthcare data usable, so the people responsible for care and cost decisions can act faster, with confidence. They help health plans break down data silos to create a single, trusted data foundation. Client's platform enables GenAI use cases by delivering clean, connected, and reliable healthcare data to support automation, prioritization, and decision workflows, and it's why client is leading the way. Client embraces the thoughtful use of AI and automation to drive innovation and efficiency.

About the Role:
The Sr. Forward-Deployed AI Engineer is a senior individual contributor responsible for designing and building the AI and GenAI/agentic systems that power their platform on Snowflake, and for carrying that work directly into client environments. Hands-on Snowflake expertise is essential to this role. Databricks experience is a plus but not required.

This is a forward-deployed role: the person in this seat spends a meaningful share of their time working inside client environments, not just building systems that clients eventually use. Candidates must bring prior, evidenced experience doing this kind of work already.

The role's focus will shift over time. Initially, the work is centered on configuring the Snowflake semantic layer and delivering metrics and reporting off of it for clients. As that foundation matures, the role expands into agentic AI work, which depends directly on the semantic layer already being correctly modeled and configured.

On client engagements, this person is the senior-most AI technical voice, trusted to work through ambiguous problems and help shape the technical vision and roadmap for AI components. This is technical leadership, not engagement ownership or people management; someone else owns the engagement and its reporting, and this role does not have direct reports.

This role blends:
AI/ML architecture and model strategy on Snowflake
GenAI and agentic systems design
Direct, hands-on client engagement and implementation

Unlike a purely internal engineering role, this position regularly puts you in front of clients: scoping their needs, implementing solutions in their environment, and troubleshooting alongside them. Candidates should bring demonstrated experience doing this kind of work already, not only an interest in doing it.

Your day to day
Semantic Layer & Metrics Reporting (initial focus)
Design, configure, and maintain the Snowflake semantic layer (e.g. Cortex Analyst semantic views), modeling business logic and metrics definitions accurately against client requirements

Build and deliver metrics and reporting for clients off of the configured semantic layer
Work directly with client stakeholders to define metrics requirements, validate outputs against business logic, and resolve discrepancies

Write efficient, production-grade SQL and Python to support semantic layer configuration, metrics pipelines, and reporting delivery

GenAI & Agentic Systems (as the role evolves)
Design and implement retrieval-augmented generation (RAG) systems, agentic workflows, and MCP-based tooling that draw on the semantic layer as their source of truth

Build and maintain evaluation harnesses to test and monitor AI/agent quality and performance
Apply sound judgment on model selection, prompting strategy, and system design tradeoffs
Own technical architecture and model strategy decisions for AI systems built on the Snowflake platform

Client-Facing / Forward Deployed Engineering
Serve as the senior-most AI technical voice on client engagements, helping shape technical direction and roadmap for AI components, without owning the engagement or its reporting

Work directly with client stakeholders to scope AI/ML use cases, translate business requirements into technical solutions, and set realistic expectations on scope and timeline

Implement and configure solutions within client environments, adapting the platform to client-specific data and constraints

Serve as the technical point of contact for clients during implementation, including troubleshooting issues live with client teams

Manage client relationships and expectations with the same rigor applied to the technical work itself

Cross-Functional Collaboration
Partner with product, data science, and operations teams to move AI/ML work from prototype to production
Communicate technical tradeoffs clearly to both technical and non-technical audiences, internal and client-facing

Contribute to team best practices for AI engineering and client implementation work

What You Bring To The Team
8+ years of software/AI engineering experience, including hands-on design of AI/ML systems
Deep, hands-on experience with Snowflake, including Snowpark, Snowflake Cortex, and Snowflake ML
Hands-on experience building and maintaining semantic layers on Snowflake (e.g. Cortex Analyst semantic views), including modeling business logic and metrics for AI/agent consumption

Experience delivering metrics and reporting off of a semantic layer for clients, including validating outputs against business logic

Demonstrated experience designing and building GenAI/agentic systems: RAG, MCP-based tooling, agent orchestration patterns, and evaluation harnesses

A proven track record working directly with external clients or customers in an implementation, forward-deployed, or professional-services capacity, with specific engagements you personally owned, not only internal engineering experience

Demonstrated ability to operate as the senior technical voice on ambiguous problems, with sound judgment on AI technical direction, without needing close direction

Strong Python engineering skills, including API design and delivery of production AI/ML pipelines
Strong communication skills, with demonstrated ability to translate technical tradeoffs for non-technical, client-facing audiences

Comfort operating with a degree of ambiguity typical of client environments, and sound judgment on when to escalate versus resolve independently

What We Would Like To See, But Not Required
Hands-on experience with Databricks, including Unity Catalog, Delta Lake, and MLflow
Healthcare payer or provider claims data experience
Experience with classical ML and with model fine-tuning
Relevant certifications (e.g. SnowPro

Thanks & Regards,

Pavan Raikhelkar

LEAD TALENT ACQUISITION SPECIALIST

Direct Number:-

Fax :

Email:

Website:

(An E-verify Company)

NOTE: We respect your online privacy. This is not an unsolicited mail. Under bill 1618 title III passed by the 105th us congress this mail cannot be considered Spam as long as we include contact information and a method to be removed from our mailing list. If you are not interested in receiving our e-mails, please reply with a "REMOVE" in the subject line. We apologize for any inconvenience caused by this mail.