Forward Deployed AI Engineer

Skills

About the Role

You will work directly with internal stakeholders to identify opportunities to improve workflows with AI. You will translate business challenges into production-ready AI agents, assistants, integrations, retrieval capabilities, and automated workflows. You will ensure solutions are secure, reliable, measurable, and reusable, while monitoring their performance and business impact.

Requirements

• Bachelor’s degree in computer science, engineering, or a related technical discipline.
• 4+ years of professional software engineering experience building, deploying, and supporting production applications.
• Strong proficiency in Python and experience building APIs, integrations, and automation.
• Hands-on expertise with large language models, model APIs, AI agents, and agentic workflows.
• Experience building and deploying enterprise AI agents or workflows using Glean, Claude, or comparable platforms.
• Knowledge of retrieval-augmented generation, embeddings, semantic or hybrid search, prompt engineering, context engineering, and structured outputs.
• Experience integrating Microsoft 365, Salesforce, Slack, Jira, Confluence, ERP systems, and modern data platforms.
• Understanding of enterprise application security, authentication, authorization, OAuth/OIDC, role-based access controls, and permission-aware data access.
• Familiarity with MCP or similar integration standards.
• Knowledge of vector databases, enterprise search, and knowledge retrieval systems.
• Proficiency with automated testing, version control, CI/CD, application observability, and production support.
• Written and verbal English communication skills.

Responsibilities

• Identify high-value AI opportunities that improve business workflows.
• Translate business challenges into practical production-ready AI solutions.
• Design, build, and deploy AI agents, assistants, and automated workflows.
• Build secure integrations between AI solutions and enterprise systems and data.
• Build retrieval and grounding capabilities for structured and unstructured enterprise data.
• Incorporate authentication, authorization, audit logging, observability, cost controls, and human oversight.
• Build automated evaluations and regression tests, and support deployment and monitoring.
• Measure solution impact through business outcomes.
• Create reusable components, templates, reference architectures, and documented patterns.
• Stay current with AI models, agent frameworks, integration standards, and enterprise AI practices.

Benefits

• Bonus
• Equity