Forward Deployed AI Engineer

Accesa; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

Accesa is seeking a Forward Deployed AI Engineer who embeds with client teams to translate real business problems into deployed ML solutions. The role blends hands-on engineering with consulting within a data and AI practice, supporting customers across industries in a remote setup.

You will prototype models, deploy them, and integrate with client data stacks, while communicating progress and trade-offs clearly to both technical and non-technical stakeholders.

Qualifications

• Strong Python programming and ML framework experience.
• Experience moving models to production (packaging, deployment, monitoring).
• Comfort working with clients in a delivery/embedded engineering role.
• Solid data engineering basics: pipelines, storage, APIs.
• Ability to adapt AI tooling to customer constraints.

Responsibilities

• Identify and frame high-value AI/ML use cases with client stakeholders.
• Prototype and ship production-grade models in customer environments.
• Integrate models with data infrastructure and apps.
• Provide hands-on deployment guidance.
• Iterate models based on end-user feedback.
• Communicate progress and trade-offs to engineers and non-technical audiences.

Skills

Python

ML frameworks

Model deployment

Data engineering basics

Client communication

System integration

Job description

Role overview

A Forward Deployed AI Engineer embeds with client teams to translate real business problems into deployed machine learning and AI solutions. The role combines hands-on engineering with delivery-style consulting, sitting within a data and AI practice of an IT services organization and supporting customers across different industries. The position is remote and based in Romania.

Responsibilities

• Partner with client stakeholders to identify and frame high-value AI and machine learning use cases
• Design, prototype, and ship production-grade models and AI systems within customer environments
• Integrate models, pipelines, and supporting services with existing data infrastructure and application stacks
• Provide hands-on technical guidance during deployment, integration, and post-launch tuning phases
• Translate feedback from end users and operators into iterative model and system improvements
• Communicate progress, trade-offs, and technical decisions clearly to both engineers and non-technical audiences

Requirements

• Strong programming skills, typically in Python, and working familiarity with mainstream machine learning frameworks
• Experience moving models from prototype to production, including packaging, deployment, and monitoring
• Comfort working directly with customers in a delivery, consulting, or embedded-engineering capacity
• Solid understanding of data engineering fundamentals such as pipelines, storage, and APIs
• Ability to adapt general AI tooling and techniques to the constraints of a specific customer environment

Nice to have

• Exposure to multiple industry verticals or a range of client engagement styles
• Familiarity with MLOps practices, cloud platforms, and modern AI infrastructure