Forward Deployed Engineer

Role: Forward Deployed Engineer
Location: Remote USA (but someone who is willing to relocate to Minneapolis will also preferred)

Role Overview:

Key Responsibilities:
1. Business Embedding and Outcome Ownership

• Embed with business and engineering teams to own AI outcomes within a defined business domain.

• Build and deliver AI solutions hands-on; this is an execution role, not an advisory role.

• Convert AI potential into production value through code-first delivery and active repository contributions.

2. Problem Discovery and Solution Design

• Understand business processes, pain points, systems, data flows, and success metrics.

• Translate problems into MVPs, integrations, automations, and production-ready solutions with an ownership mindset

• Build across APIs, databases, cloud platforms, workflow tools, enterprise systems, and AI/GenAI technologies.

3. Rapid Prototyping and Value Validation

• Own the journey from discovery to working solution, rapidly proving business value through pilots and POCs

4. Integration, Adoption, and Scale

• Integrate with enterprise platforms, data systems, workflows, collaboration tools, and third-party APIs.

• Document architectures, implementation playbooks, reusable components, and customer-specific solution guides.

• Feed field learnings into product roadmap, accelerators, and go-to-market propositions.

Required Skills and Experience:

• 4 8 years of experience in AI led engineering, implementation, product, consulting, or customer-facing technology roles.

• Strong engineering fundamentals with hands-on coding experience in Python, JavaScript/TypeScript, Java, .NET/C#, or Go.

• AI proficiency is mandatory; candidates may come from software engineering, data science, UX, or related domains with proven hands on experience.

• Daily AI tool usage, demonstrable code contributions, and documented token usage.

• Strong analytical thinking and expertise in effectively utilizing data to derive AI solutions to solve business problems.

• Strong understanding of APIs, databases, cloud services, authentication, integrations, and deployment.

• Experience in Data and analytics platforms.

• Comfortable with structured and unstructured data.

• Experience with GenAI, LLMs, RAG, agents, AI workflow automation, prompt engineering, model integration and model training.

• Cloud experience across AWS, Azure, or Google Cloud.

• Good communication, adaptability, and problem-solving in ambiguous environments.

Good to Have:

• Knowledge of ML algorithms, model building, deployment, deep learning, and NLP.

• Experience integrating with Salesforce, Jira, Rally, Oracle, ServiceNow, Microsoft Dynamics or similar platforms.

• Familiarity with data engineering, ETL/ELT pipelines, BI dashboards, analytics, and reporting workflows.

• Healthcare exposure, especially contact centers, claims automation, finance, or technology services.