What does a Forward Deployed Engineer do at Realign?

Forward Deployed Engineers at Realign are embedded directly with enterprise customers to implement and customize the platform, build integrations, and solve technical problems in real-world client environments. They combine software engineering skills with direct customer interaction, writing production code while also serving as the primary technical point of contact.

What skills are required for this Forward Deployed Engineer (Contract / Full Time) role?

The Forward Deployed Engineer (Contract / Full Time) role at Realign typically requires strong software engineering skills (Python, TypeScript, SQL), experience with APIs and system integration, comfort with cloud platforms, and the ability to communicate complex technical topics to non-technical stakeholders. Most FDE roles require 3-7 years of engineering experience.

Is this Forward Deployed Engineer position at Realign remote?

Remote availability varies by Realign FDE role. Some positions are fully remote, others require travel to customer sites, and some are hybrid. Check the full job description above for the specific location requirements for this posting.

Realign

Forward Deployed Engineer (Contract / Full Time)

Remote Mid Posted September 09, 2026

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Role Description

Remote, Ohio 43016 Posted September 10th, 2026

Job Type: Full Time

Job Category: IT

Job Description

Role -Forward Deployed Engineer (Contract / Full Time) Location – Remote Contract & FTE Both ======================================================================================================================

Role Overview:

We are looking for a Forward Deployed Engineer (FDE) who partners directly with UHG 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.

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.

Required Skills

Cloud Developer DevOps Engineer

About Forward Deployed Engineering

Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.

Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.

Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.