Software Engineer, Applied AI
Build and deploy production-grade AI agents and machine learning solutions for strategic enterprise customers, partnering with their technical teams from prototype through operation. Requires 3+ years of software engineering experience, strong programming and data workflow skills, and hands-on LLM or ML application development.
About the job
Responsibilities
- Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents.
- Build and iterate on AI pipelines and agents from prototype to production.
- Contribute to evaluation frameworks and observability infrastructure.
- Rapidly design, iterate, and ship high-quality code and machine learning pipelines.
- Translate ambiguous business objectives into robust, scalable, and performant solutions using Python and SQL.
- Own the full lifecycle of AI solution implementation, including prototyping, deployment, monitoring, and optimization in secure, large-scale production environments.
- Partner directly with customer data science and engineering teams as a technical expert and trusted advisor.
- Implement data validation, SLA observability, and management of complex system interdependencies.
- Collaborate with Product and Engineering teams and share customer feedback to influence the AI platform.
Requirements
- Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
- 3+ years of professional software engineering experience.
- Passion for solving complex and ambiguous technical challenges using research and AI.
- Hands-on experience building and shipping LLM-based or machine learning applications.
- Experience with data modeling, ETL/ELT development, and performance tuning.
- Advanced proficiency in Python, Java, C++, or other backend programming languages, including scripting and automating data workflows.
- Strong problem-solving and communication skills.
- Ability to adapt quickly to the changing generative AI landscape.
Nice-to-haves
- Experience building production applications using LLMs, especially RAG and agentic workflows.
- Experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment such as AWS, Azure, or Google Cloud.
- Understanding of data warehousing principles, architecture, and best practices.
- Customer-facing experience, such as in a solutions architect role.
- Startup experience.
Compensation and Benefits
- The role offers the opportunity to work with AI tools and technologies, solve complex enterprise challenges, and contribute to Snowflake's AI platform.
Skills
Python, SQL, Java, C++, LLMs, Machine Learning, RAG, AI Agents, ETL, ELT, MLOps, AWS, Azure, GCP, Data Warehousing
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