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# AI Engineer

- **Company:** Robotics Technologies LLC
- **Location:** Dallas, TX, United States
- **Experience:** Experienced
- **Salary:** $162,240.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, JIRA, User Authentication, Microsoft Azure, C Sharp (Programming Language), Cloud Computing, Encodings, Collaborative Software, Databases, Information Engineering, Extract Transform Load (ETL), Data Systems, Python (Programming Language), Machine Learning, Microsoft Dynamics, Oracle (Applications), Rapid Prototyping Process, Cloud Services, Salesforce.Com, Software Engineering, Data Streaming, Systems Integration, TypeScript, Unstructured Data, Workflow Management Systems, Google Cloud, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Data Analytics, Software Coding, Data Pipelines, Servicenow, Web Api
- **Published:** November 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5aa21d8a695e172d

## About the Role

* 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
Equal Opportunity Employer

## Description

We are looking for a Forward Deployed Engineer (FDE) who partners directly with business teams to identify high-value problems and deliver AI-led automation and innovation under centralized council oversight.

An FDE in UHG is an empowered AI builder embedded within business units to understand real-world context, build practical solutions, and drive measurable AI driven outcomes.

The role blends hands-on engineering, solution architecture, product thinking, consulting, and customer-facing execution., 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.