Software Engineer - Cloud​/AI Specialist

Overview

STC, a wholly owned subsidiary of Arcfield, was founded to do systems engineering differently. As an industry-leading solutions provider in digital engineering and model-based systems engineering (MBSE), the company delivers MBSE-as-a-Service, integrated digital engineering environment deployments, training and consulting to both commercial and public sector customers. Every day, STC's team of expert engineers are unleashing the power of digital engineering to navigate complexity, increase understanding and inform decision-making.

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This position is for Strategic Technology Consulting (STC), an Arcfield Company. In this role, the candidate will be a key contributor to a fast‑paced cross‑functional software and AI team developing cloud‑based AI applications. They will collaborate closely with front‑end, back‑end, and AI/ML pipeline engineers to design, build, and deploy scalable solutions. The candidate will work closely with stakeholders to understand use cases and needs.

He or she will take ownership of translating requirements into well‑defined and scalable software architectures, integrating AI services and cloud infrastructure. They will participate in agile workflows, contribute to continuous improvement, and ensure seamless and secure delivery of products to various environments. Occasionally, customer‑facing demonstrations of software technology are required. Additional duties as assigned.

The candidate should be motivated and success oriented. He or she should exhibit expertise in:

• AI Application Design & Development – Design, develop and integrate cloud‑based applications leveraging Azure and AWS AI/ML services (e.g., OpenAI, Bedrock), and related APIs, with a focus on generative AI models and large language model integration
• ML Ops & Data Pipeline Experience – Builds and optimizes cloud‑hosted data ingestion, transformation, and storage pipelines for AI workloads. Work closely with data scientists and ML engineers to provision scalable cloud hosting environments
• Integration – Connect AI services with front‑end applications, back‑end systems, and client APIs to deliver end‑to‑end solutions.
• Problem Solving & Innovation – Identify challenges in AI service integration and propose creative, practical solutions.
• Stakeholder Engagement – Communicate technical progress, risks, and solutions clearly with stakeholders and team members.
• Continuous Improvement – Stay current with evolving Azure and AWS AI services, bringing forward best practices and new capabilities.

Required Skills

• BS 8‑10, MS 6‑8, PhD 3‑5
• Must be able to obtain and maintain a Secret Clearance

Technology and Tools

The ideal candidate should demonstrate proficiency in the following technologies:

• Software Engineering (Java, Python)
• Graph‑Based/Data Science Skills (PyGraph, Pydantic)
• AI/MLOps – (Azure AI Studio; AWS Sagemaker, Kubeflow, etc)
• Familiarity with LLM APIs (OpenAI API, AWS Bedrock/boto)
• Experience with modern LLM integration methods and applicable tools (Llama Index/Lang Chain)
• Orchestration frameworks (RAG, Agents, MCP, etc)
• Retrieval (Vector Search, BM25, Hierarchical, etc)
• Experience with Software Development lifecycle practices and automations (Pipeline design, management, Git/Git Ops, CI/CD, Version Control, Testing)
• Experience implementing authentication/authorization and role-based access controls (RBAC)
• Cloud‑native technologies and development (Python/FastAPI, SQL, Redis)
• Command‑line (CLI) Proficiency (Bash, Power Shell, etc)
• Experience integrating software via RESTful APIs, Java APIs, Web Sockets, Async Message Queues (Pub/Sub, etc)

Preferred Technologies

In addition, experience with the following is highly desirable:

• Software application design and development using UML or SysML
• Performance Optimization (server‑side rendering, code splitting, CSS modules)
• Systems Engineering processes, methods, and tools as applied to systems life cycles
• Digital Engineering methodologies and tooling
• Familiarity with cloud security frameworks and compliance requirements (e.g., NIST, DoD STIGs)
• Proficiency with infrastructure‑as‑code tools (Terraform, Ansible, Cloud Formation) for controlled…