Forward Deployed Engineer (AI and Automation)
New
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLGCPCI/CDRESTful APIsLLM
Requirements
- 5+ years of software engineering experience delivering production-grade systems used in real-world environments.
- Strong experience with Python or equivalent languages such as TypeScript or Go.
- Hands-on experience with AI/LLM technologies, including APIs, agent frameworks, RAG patterns, or similar architectures.
- Familiarity with LLM evaluation techniques such as prompt testing, human-in-the-loop validation, and automated evaluation pipelines.
- Experience integrating systems via REST APIs, SQL-based data pipelines, and cloud platforms (AWS or GCP).
- Strong product mindset with the ability to identify problems, design solutions, and communicate trade-offs effectively.
- Experience working across organizational boundaries and collaborating with both technical and non-technical stakeholders.
- Strong understanding of modern software engineering practices, including testing, CI/CD, and scalable system design.
- Excellent communication skills and ability to influence decisions at multiple levels of the organization.
- Demonstrated ability to work in fast-moving, ambiguous environments with high ownership expectations.
Responsibilities
- Design, build, and deploy production-grade AI agents and automation systems that solve complex operational challenges across business functions.
- Partner with subject matter experts to translate manual workflows into scalable, reliable agentic architectures.
- Rapidly prototype and deliver end-to-end AI solutions using Python and modern LLM-based frameworks, from discovery through production deployment.
- Integrate AI systems with enterprise platforms such as Salesforce, Workday, and other core SaaS tools while ensuring secure and reliable data flow.
- Develop monitoring, evaluation, and guardrail systems to ensure AI solutions are accurate, compliant, and production-safe.
- Lead structured discovery sessions with stakeholders to define requirements, feasibility, and technical scope before implementation.
- Build evaluation frameworks to measure agent performance, detect regressions, and ensure ongoing system reliability.
- Enable internal teams by sharing knowledge, building reusable tooling, and promoting AI-first automation practices across the organization.
- Maintain clear documentation, architecture decisions, and reusable playbooks to support long-term scalability and adoption.
- Collaborate with legal, security, and engineering teams to ensure responsible handling of data, including PII and compliance requirements.
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