Forward Deployed Engineer - AI
A customer-facing engineer designs and builds AI solutions (agents, RAG pipelines, LLM integrations) while advising enterprises on AI governance, security, and trust—traveling 40% to embed with clients.
- Compensation
- Not disclosed
- Location
- Jersey City, NJ, United States
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- 5+ years in software engineering, solutions architecture, or technical consulting, with at least 2 years hands-on with modern AI/LLM systems in real projects (not only experimentation).
- Practical experience building with LLM APIs and frameworks (e.g., Azure OpenAI, Bedrock, Vertex, LangChain, Semantic Kernel) and patterns such as RAG, agentic workflows, and tool/function calling.
- Machine Learning Expertise: Hands-on machine learning experience spanning model development, evaluation, deployment, and operationalization, with a focus on enterprise AI solutions, predictive analyti
- Strong programming skills in Python and/or C#/TypeScript, plus working fluency with at least one major cloud platform (Azure, AWS, or GCP), including identity, networking, and data services.
- Demonstrated ability to scope technical projects from ambiguous business requirements: you can run a requirements workshop, challenge assumptions constructively, and produce a credible plan with phase
- Excellent communication in front of senior stakeholders — you can explain why AI governance matters to a board member and debate vector database trade-offs with a platform engineer in the same meeting
- Willingness to travel to client sites and to operate with high autonomy in ambiguous, fast-moving engagements.
Full description
About AvePoint
AvePoint is the global leader in data protection, unifying data security, governance, and resilience to provide a trusted foundation for AI. More than 28,000 customers rely on the AvePoint Confidence Platform to secure, govern, and rapidly recover data across Microsoft, Google, Salesforce, and other cloud environments. With a single platform for lifecycle control, multicloud governance, and rapid recovery paired with clear ownership across the business, we prevent overexposure and sprawl, modernize legacy and fragmented data, and minimize data loss and interruption. Our global partner ecosystem includes approximately 6,000 MSPs, VARs, and SIs, and our solutions are available in over 100 cloud marketplaces. To learn more, visit .
About the Role
Enterprises are adopting AI faster than they can govern it — and they are looking for a partner who can do two things at once: speak credibly about AI trust, governance, and security, and actually build. The Forward Deployed Engineer (AI) is that partner.
You are the technical face of AvePoint inside client organizations: equally comfortable whiteboarding AI trust and governance concepts with a CISO, translating a business problem into a scoped AI build project, and writing the first working prototype yourself. You embed with clients, ship real outcomes, and own the engagement end to end.
This is not a pre-sales role with a demo script, and not a back-office delivery role. It is the engagement model pioneered by leading AI companies for their strategic enterprise customers: a senior engineer deployed forward, with the autonomy to own the problem from first workshop to production.
What you'll do
Advise on AI trust and governance.
Lead workshops that help clients understand and take control of their AI landscape — agents, copilots, models, and the data behind them, including the shadow AI they didn't know about. Explain AI governance, security posture, and resilience concepts credibly to both technical teams and executives. Guide clients through obligations such as the EU AI Act, NIS2, and ISO 42001, and help them stand up practical operating models: AI inventories, approval workflows, risk classification, and audit evidence.
Scope and shape AI build projects.
Sit with business stakeholders to understand the underlying need behind "we want AI for X." Identify the highest-value use cases, define success criteria, and translate ambiguous requirements into concrete, estimable technical scopes — architecture outlines, data and integration requirements, delivery phases, effort and risk assessments. Write statements of work that engineering teams can actually deliver and clients can actually sign.
Build and deliver.
Develop prototypes and production components for client AI solutions: agent workflows, RAG pipelines, LLM integrations (Azure OpenAI, AWS Bedrock, Google Vertex, Anthropic), MCP-based tool integrations, and the governance and security controls around them. Deliver custom adapters and local tooling for regulated, cloud-restricted, or air-gapped environments where standard SaaS approaches cannot go.