Field-Embedded AI Solutions Engineer

Analog Devices, Inc.; San Jose (CA); Híbrido; USD 198 000 - 272 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

medical coverage

vision and dental coverage

401k

paid vacation

holidays

sick time

other benefits

Resumo da oferta

Analog Devices, Inc. is seeking a Staff Forward Deployed Engineer who blends software engineering with technical consulting to embed within strategic customers and scale ADI’s AI and edge technologies in real-world infrastructures.

You will write production-grade data pipelines, integrate APIs, and drive MLOps/AI workflows on-site with clients, collaborating across multiple internal teams. Travel to client sites (about 10%) is required to frame problems and own architecture for measurable

Qualificações

• BS/MS in CS, EE, or related field.
• 10+ years building production software.
• Experience deploying on-site in complex enterprise environments.
• Excellent communication; able to translate customer needs into architecture.
• Willingness to travel 10% of the time.

Responsabilidades

• Write production-grade code: custom data pipelines, API integrations, and MLOps/AI workflows.
• Embed on-site with clients, partnering across CSG, VBU, SDP, and Edge AI teams.
• Frame customer problems and own the architecture that solves them.
• Drive rapid iteration and field learning to deliver measurable customer outcomes.
• Produce reusable, ADI-owned assets that create defensible wedges.

Conhecimentos

Python

Data pipelines

APIs

MLOps/AI workflows

Communication skills

Willingness to travel

Formação académica

BS/MS in CS, EE, or related

Descrição da oferta de emprego

Analog Devices, Inc. is seeking a Staff Forward Deployed Engineer who blends software engineering with technical consulting to embed within strategic customers and scale ADI’s AI and edge technologies in real-world infrastructures.

You will write production-grade data pipelines, integrate APIs, and drive MLOps/AI workflows on-site with clients, collaborating across multiple internal teams. Travel to client sites (about 10%) is required to frame problems and own architecture for measurable