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# Forward Deployed Engineer

- **Company:** Avanciers LLC
- **Location:** Houston, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Code Review, Continuous Integration, Cursor, Distributed Systems, Monitoring of Systems, OpenShift, Payment Gateway, Software Deployment, Software Engineering, Google Cloud, Spring Cloud, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, AI Coding Agents, Infrastructure as Code (IaC), Backend, Agentic-AI, Containerization, AI Platforms, Amazon Bedrock, Deployment Automation, CrewAI, Nintex, Machine Learning Operations, Artificial Intelligence Governance, Semantic Kernel, Devsecops
- **Published:** October 18, 2026
- **Apply:** https://www.dice.com/job-detail/1f12d99d-fd41-4b48-9c80-a0413f18d7ba

## About the Role

Avanciers seeking experienced Forward Deployed Engineers (FDEs) with strong expertise in AWS, Amazon Bedrock, Java, and Agentic AI to design, develop, and deploy enterprise-grade AI solutions. Candidates must have banking or financial services experience, preferably in payment gateways, payment processing, or card management systems.

The ideal candidate will combine hands-on software engineering, AI solution architecture, and customer-facing delivery experience to transform business requirements into secure, scalable, production-ready AI applications., * 10+ years of overall software engineering experience.

 * Mandatory hands-on experience with AWS, Amazon Bedrock, and Java.
 * Mandatory banking or financial services domain experience.
 * Experience with payment gateways, payment processing, or card management systems is highly preferred.
 * Strong knowledge of backend development, APIs, distributed systems, system design, and enterprise architecture.
 * Hands-on experience with Agentic AI frameworks such as LangGraph, Semantic Kernel, CrewAI, n8n, or similar.
 * Experience with LLMs, prompt engineering, RAG, context engineering, memory systems, and AI tool integration.
 * Experience developing and deploying production-grade cloud-native applications.
 * Knowledge of CI/CD, IaC, DevSecOps, automated testing, and deployment automation.
 * Familiarity with AI coding tools such as Cursor, GitHub Copilot, or equivalent platforms.
 * Understanding of AI governance, security controls, model monitoring, and compliance in regulated environments.
 * Excellent communication, analytical, problem-solving, and stakeholder management skills.

Preferred Qualifications

 * Experience in customer-facing engineering, Forward Deployed Engineering, Solutions Engineering, or Field Engineering roles.
 * Experience building multi-agent systems and enterprise AI platforms.
 * Knowledge of AI governance frameworks, model risk management, and financial services compliance.
 * Experience creating reusable architecture patterns, solution accelerators, and engineering standards.
 * Familiarity with Azure, Google Cloud Platform, or OpenShift., The ideal candidate is a hands-on engineering professional who can bridge business requirements and technical implementation, build Agentic AI solutions using AWS Bedrock and Java, and deliver secure, scalable, production-ready applications within a banking or financial services environment.

## Description

* Collaborate with business stakeholders, product owners, and engineering teams to translate business requirements into AI-driven solutions and system architectures.
 * Own end-to-end solution delivery, from discovery and prototyping to MVP development, production deployment, and optimization.
 * Design and implement agentic AI workflows and multi-agent systems using LangGraph, Semantic Kernel, CrewAI, or similar frameworks.
 * Develop LLM-powered solutions using Amazon Bedrock, foundation models, prompt engineering, RAG, context engineering, and tool integration.
 * Build scalable backend services, APIs, and complex enterprise integrations using Java and cloud-native technologies.
 * Implement AI guardrails, human-in-the-loop workflows, validation mechanisms, and fallback strategies.
 * Leverage AI-assisted development tools such as Cursor and GitHub Copilot for coding, testing, code reviews, and documentation.
 * Design and maintain CI/CD pipelines, Infrastructure as Code (IaC), and DevSecOps/MLOps practices.
 * Ensure compliance with enterprise security, data privacy, AI governance, model risk management, and regulatory requirements.
 * Present technical solutions and architecture decisions to business stakeholders and engineering leaders while driving continuous improvement.