Forward Deployed Engineer, Applied AI
Build and deploy production-grade AI agents and ML solutions for strategic enterprise customers, partnering with their technical teams and advising on AI adoption. The role requires 3+ years of software engineering experience, hands-on LLM or ML application development, and strong data engineering skills.
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 to ensure reliable AI performance.
- 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 AI research and emerging technologies.
- 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 another backend programming language, with experience scripting and automating data workflows.
- Excellent problem-solving and communication skills.
- Ability to adapt quickly in a fast-paced Generative AI environment.
Nice-to-haves
- Experience building production applications using LLMs, including RAG and agentic workflows.
- Experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment.
- Strong understanding of data warehousing principles, architecture, and best practices.
- Experience in a customer-facing role, such as solutions architecture.
- Startup experience.
Compensation and Benefits
- The posting directs candidates to Snowflake's Careers Site for salary and benefits information for jobs located in the United States.
Skills
Python, SQL, Java, C++, LLMs, Machine Learning, RAG, AI Agents, MLOps, ETL, Data Modeling, Data Warehousing, AWS, Azure, GCP
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