Senior AI Engineer - Cortex Code

Senior AI Engineer, Enterprise

Snowflake

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. ABOUT THE TEAM The GTM AI Engineering team is part of Snowflake's Data Analytics & AI organization and builds the internal AI platform and intelligent applications that power Snowflake's Go-To-Market organization. We develop production-grade AI systems used daily by thousands of employees across Sales, Solution Engineering and Marketing. As a Senior AI Engineer, you will build the backend services, platform infrastructure, and AI capabilities that power next-generation agentic applications. You will work at the intersection of distributed systems, backend engineering, applied AI, and internal product development, designing scalable, reliable, and secure systems that enable intelligent experiences with direct business impact. This role offers the opportunity to shape a rapidly growing internal AI platform, establish engineering best practices, and build AI products that directly accelerate Snowflake's growth. IN THIS ROLE, YOU WILL: * Design and build scalable backend services and distributed systems that power production AI and agentic applications. * Develop APIs, orchestration services, and platform capabilities for internal AI products. * Build reusable AI platform components, including agent frameworks, evaluation pipelines, developer SDKs, templates, and shared Python libraries. * Evolve our engineering platform through modern CI/CD pipelines, automated testing, deployment tooling, and developer experience improvements. * Optimize AI infrastructure and backend services for performance, scalability, reliability, security, and cost efficiency. * Leverage Snowflake Cortex, Snowpark, and the broader Snowflake AI ecosystem to build intelligent data applications. * Establish engineering best practices around testing, observability, monitoring, evaluation, and production readiness for AI systems. * Collaborate closely with product managers, data engineers, designers, and GTM stakeholders to deliver high-impact AI solutions. * Stay current with advances in AI engineering and help drive adoption of modern technologies, frameworks, and development practices across the team. WE WOULD LOVE TO HEAR FROM YOU IF YOU HAVE: REQUIRED QUALIFICATIONS * 5+ years of professional software engineering experience with a Bachelor's degree (or higher) in Computer Science, Software Engineering, or a related technical field. * Strong proficiency in Python (3.11+) with experience writing production-quality, well-tested, maintainable code using modern Python practices. * Experience building RESTful APIs and backend services using FastAPI or similar frameworks. * Experience designing and operating distributed systems and microservice architectures. * Strong understanding of software engineering best practices, including testing, CI/CD, code quality, observability, and production operations. * Experience with containerization technologies such as Docker. * Hands-on experience building production AI applications using large language models. * Experience with prompt engineering, tool calling, structured outputs, and LLM orchestration frameworks. * Strong problem-solving skills, excellent communication, and the ability to work effectively in a fast-paced, collaborative environment. PREFERRED QUALIFICATIONS * Experience with the Snowflake platform, including Snowpark, Cortex AI, Snowpark Container Services, Snowflake Connector, and Snowflake CLI. * Experience building agentic applications using frameworks such as LangGraph or similar multi-agent orchestration frameworks. * Experience with AI evaluation, monitoring, guardrails, and observability frameworks. * Familiarity with model serving, inference optimization, and AI infrastructure. * Experience working with large-scale data platforms, ETL pipelines, and modern data engineering workflows. * Experience building full-stack AI applications using Streamlit, React, or similar frameworks. * Experience with Python data libraries such as pandas and NumPy. * Familiarity with modern AI development tools such as Cursor, Claude Code, Cortex Code, or similar AI-assisted development environments. * Experience working with cloud-native infrastructure and Kubernetes is a plus. WHY JOIN OUR TEAM? Joining the GTM AI Engineering team means building AI products that have a direct and measurable impact on Snowflake's business. You'll help define the architecture of our internal AI platform while developing intelligent applications used every day by thousands of employees across our global Go-To-Market organization. This is a unique opportunity to combine backend engineering, distributed systems, and applied AI to solve real business problems at scale. You'll work alongside world-class engineers and product leaders while helping shape how AI transforms the way Snowflake operates internally. If you're passionate about building production-grade AI systems, enjoy solving complex engineering challenges, and want to help define the future of enterprise AI, we'd love to hear from you. Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

Senior/ Staff Analyst, Finance Analytics & AI

Snowflake

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. ABOUT THE ROLE We are an AI-first analytics team. We don't use AI to augment traditional BI workflows — we've replaced them. The Finance Analytics team builds the intelligence layer that Strategic Finance runs on: AI agents that encode repeatable finance processes, Streamlit apps that surface real-time insight, semantic models that let any analyst query complex data in plain English, and workflow automations that collapse hours of manual work into a single prompt. Our primary development environment is CoCo (Cortex Code), Snowflake's AI coding assistant, and SnowWork, the AI IDE we ship work in. Every deliverable on this team is built AI-first: you design the workflow, you write the prompt, you validate the output. If you are still building dashboards by hand, refreshing Excel files manually, or treating AI as a spell-checker for your code — this role will ask you to operate differently. This is a high-breadth seat. One week you're building a new AI agent for quarterly revenue analysis; the next you're designing a sensitivity analysis tool for an earnings war room. You are equally comfortable in an AI-IDE, a Python file, and a stakeholder summary for a senior finance leader. WHAT YOU'LL WORK ON AI AGENT AND WORKFLOW DEVELOPMENT (PRIMARY FOCUS) * Design and build skills and agentic experiences that encode repeatable finance workflows — revenue analysis, cost monitoring, earnings prep, headcount tracking — into reusable, invokable tools using CoCo and CoWork * Write and iterate on prompt & skill structures (YAML + Markdown skill files) based on output quality and stakeholder feedback * Build skills that allows non-technical finance analysts to produce analyst-quality output in a single prompt * Evaluate model outputs rigorously — you are the quality gate before anything reaches a finance stakeholder FINANCE ANALYTICS * Build and maintain quarterly and weekly revenue summary pipelines * Support sensitivity analysis models for quarterly business reviews & revenue forecast scenarios * Produce ad-hoc analysis for Strategic Finance SEMANTIC LAYER & APPLICATION DEVELOPMENT * Own semantic layers end-to-end — model design, versioning strategy, verified query coverage, and accuracy iteration based on eval metrics; not just build models, but maintain the contract between the model and its consumers across each quarterly iteration * Develop and deploy production finance dashboards as Streamlit apps (locally and deployed to Snowflake) * Build customer-facing demo applications for Sales and Field teams * Apply reusable component patterns and shared utility libraries for consistent, polished UI EARNINGS AND REPORTING AUTOMATION * Participate in quarterly earnings cycle prep — scenario tooling, export automation, IR data requests * Build and maintain source-of-truth reporting exports (multi-tab Excel, formatted to spec) * Support ad-hoc disclosure and investor relations data needs during quarter-end HARD SKILLS REQUIRED MUST-HAVE AI-assisted development — You have used an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent) as your primary development tool. You know how to write a prompt that produces production-ready output, how to steer a model that's heading in the wrong direction, and how to encode domain logic into a reusable, parameterized skill. You have a measurable, trackable record of daily AI usage. Prompt engineering and skill authoring — You can write a structured prompt (YAML + Markdown or equivalent) that routes correctly 95% of the time, handles edge cases gracefully, and encodes enough domain knowledge that the model behaves like a subject matter expert. You think in terms of context, instructions, examples, and output format — not just "the thing I typed before the code came out." Python — Modern, type-hinted, readable. You write Python-based applications, data pipelines, and reporting automation. You understand caching, session state, and how to structure a multi-page app cleanly. At the senior level: you've contributed to a shared library or package that others depend on, and you've designed agent orchestration systems — including parallel agent patterns with synthesis layers. SQL — CTEs, window functions, incremental pipeline patterns. You don't look up the syntax for a row-numbered deduplication. Data modeling fundamentals — You understand bronze, silver, and gold data models conceptually and contribute to the gold layers and how they translate to semantic layer. You know not just how to build a model, but how to version it, evaluate SQL generation accuracy, maintain a verified query library, and iterate based on real analyst feedback. A non-technical user should be able to query your model in plain English and get a correct answer. STRONG PLUS * Snowflake Cortex — Cortex Analyst, Cortex Agents, AI_SUMMARIZE, AI_EXTRACT, Dynamic Tables, semantic views * SnowWork / CoCo — Prior experience deploying agents, authoring skill files, or working within the Snowflake Intelligence ecosystem * Finance literacy — You can read a revenue waterfall, distinguish ARR from NRR, and explain what drives a QoQ change in product revenue * Reporting automation — openpyxl, multi-tab Excel exports formatted to spec, named ranges * dbt — Model authoring, ref() patterns, YAML tests in a cloud warehouse context * Semantic search / embeddings — Vector similarity, embedding-based retrieval, and how they power natural language analytics SOFT SKILLS REQUIRED TRANSLATES BETWEEN AI, DATA, AND FINANCE Your stakeholders are financial analysts and senior directors who think in Excel models and board decks. You write prompts and code, but your output needs to make sense to someone who has never opened a terminal. You are the translation layer between what the model can do and what finance actually needs. You communicate complex ideas simply, ensuring stakeholders understand, trust, and can act on what you build. You are the translation layer between what the model can do and what finance actually needs. You set the standard for how agents are built on this team. Junior analysts look to your skills and code as the reference implementation. You push back on shortcuts that create maintenance debt. You don't wait to be asked to improve shared infrastructure. THINKS IN WORKFLOWS, NOT TASKS You don't just answer a question — you build a tool that answers it forever. When asked to do something twice, you automate it. Your instinct is to encode work into a reusable agent, not to redo it manually each week. At the senior level, this extends to the team: when the team does something repeatedly, you build the shared infrastructure that makes everyone faster. WORKS FAST WITH HIGH ACCURACY The role runs on a weekly cadence tied to finance deliverables. You scope, build, and ship a working artifact in 1–2 days. Accuracy matters more than speed — but accuracy is not a reason to be perpetually slow. COMFORTABLE WITH AMBIGUITY The brief is often: "Can you build something like the earnings tool, but for sensitivity analysis?" You scope it, build a working prototype, and come back for feedback — not a list of clarifying questions. MINIMUM REQUIREMENTS * 3-5+ years of experience in analytics, data engineering, or a technical finance adjacent role * Has used an AI coding assistant as a primary development tool — daily usage, not occasional * Proficient in SQL — you can write a window function without looking it up * Has shipped multiple Python applications that end-users actually interacted with; at least one is actively maintained in production * Comfortable working in Git (PRs, branches, code review) * Familiar with fiscal year concepts and core revenue metrics (ARR, bookings, NRR) WHAT SUCCESS LOOKS LIKE AT 90 DAYS * You've taken ownership of the quarterly and weekly revenue analysis workflows — they run correctly on schedule without hand-holding * You've shipped at least one Streamlit app to production or a demo application to the Finance Workloads team * You've participated in at least one quarterly earnings cycle * You've contributed a module, skill, or shared component to the team's shared infrastructure — something other analysts use without you having to explain it WHY THIS ROLE IS UNUSUAL AT THIS LEVEL This seat asks you to do all of that and build the AI infrastructure that makes the entire Finance Analytics team faster. You are simultaneously a practitioner and a workflow engineer. If you are fluent with AI development tools, you can punch significantly above your level. At the senior level, you are not just building the infrastructure — you are deciding what it should be. That means making architectural calls that hold across quarters, not just shipping the next feature. Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

Senior Forward Deployed Engineer, Applied AI

Snowflake

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is about empowering enterprises to achieve their full potential – and people too. With a culture that's all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology – and careers – to the next level. Where Data Does More. Join the Snowflake team. At Snowflake, we are building a high-impact team to help the world's most innovative companies unlock the power of AI. As a Senior Forward Deployed Engineer, Applied AI on our Cortex AI team, you will be a hands-on technical leader and trusted partner to our most strategic customers. You will own the end-to-end delivery of enterprise AI programs, leading a team of 2–4 engineers while staying deeply technical yourself. You will set the technical direction for your customer engagements, mentor your team, and serve as the senior technical voice at the intersection of product, engineering, and customer success. IN THIS ROLE AT SNOWFLAKE, YOU WILL: Lead Customer Programs: Own the full lifecycle of complex, multi-engineer AI engagements – from scoping and architecture through deployment, monitoring, and handoff. Be accountable for delivery quality and customer outcomes for the projects you lead. Own AI Quality: Define what "good" means for each engagement. Translate ambiguous customer goals into measurable quality metrics, evaluation frameworks, and golden datasets – then run systematic eval loops to hill-climb on agent quality, catch regressions before customers do, and continuously raise the bar on accuracy, faithfulness, and safety. Set the standard for how the team measures and improves AI systems in production. Grow and Mentor Engineers: Provide day-to-day technical leadership and mentorship to a team of 2–6 Applied AI Engineers. Review designs and code, unblock teammates, and actively develop their skills and careers. Deliver with Velocity: Remain a hands-on contributor – designing, iterating, and shipping high-quality ML pipelines and agentic AI solutions alongside your team. Translate ambiguous business objectives into robust, scalable, and performant solutions. Productionize AI at Scale: Own the full implementation lifecycle for AI solutions, from prototype through deployment, monitoring, and optimization in secure, large-scale production environments. Build the safety guardrails, observability, and human-review workflows that keep AI applications reliable and trustworthy – and close the loop from production traces and user feedback back into your evals so quality compounds over time. Be a Strategic Technical Advisor: Serve as a senior technical advisor to customer data science and engineering leadership. Set the standard for how Snowflake AI is deployed and articulate complex technical concepts to both technical and executive stakeholders. Collaborate to Innovate: Work cross-functionally with Snowflake's Product and Engineering teams, bringing real-world patterns and feedback from the field to directly shape the future of Snowflake's AI platform. Drive Compounding Outcomes: Identify recurring deployment patterns and turn them into reusable assets – reference architectures, evaluation harnesses, and product feedback that scale Snowflake's impact across customers. Have the opportunity to travel: Spend at least 25% of your time onsite, working closely with Snowflake's most strategic customers. WE'RE LOOKING FOR CANDIDATES WHO HAVE: MINIMUM QUALIFICATIONS * Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing others' work, and driving delivery to completion. * Proven experience building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows. * Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems, and using evals to systematically improve quality over time. * Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to both technical and executive stakeholders. * Comfort with ambiguity and the ability to independently structure and execute on complex, open-ended problems. * 5+ years of professional software engineering experience. * Experience in a customer-facing technical role. * Willingness to travel. PREFERRED QUALIFICATIONS * Experience building eval sets from production traces and synthetic data, and running structured experimentation (A/B tests, ablations, offline evals) to compare prompts, models, or agent architectures. * Familiarity with eval and observability tooling (e.g., Braintrust, LangSmith, Arize, Weave, Promptfoo) or experience building custom eval harnesses. * Experience with failure-mode analysis on agent or RAG systems – categorizing errors (hallucination, retrieval miss, planning failure, tool misuse) and driving each down with targeted evals. * Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment (AWS, Azure, or GCP). * Familiarity with core data science libraries and tools (e.g., pandas, numpy, Snowpark). * Startup experience or experience in a high-growth, fast-paced environment. Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee’s duty to keep customer information secure and confidential. Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com