Senior AI Software Engineer

Credence•Bath Township, OH

4d•$155,000 - $200,000

About The Position

Credence is seeking a Senior AI Software Engineer for its growing AI and Automation practice. This senior technical contributor will design, develop, and deploy advanced AI-driven solutions, including generative AI, LLM-powered applications, and agentic AI systems. The engineer will collaborate with engineering, data, cloud, and stakeholder teams to deliver scalable, secure, mission-focused AI capabilities for federal clients, while helping guide technical direction, strengthen engineering practices, and mentor other engineers. The role includes developing generative AI and LLM capabilities; owning agentic AI development lifecycles; applying software engineering principles to build maintainable, testable, scalable systems; and collaborating across technical and mission teams. It also involves integrating secure cloud deployment practices through infrastructure as code, CI/CD, containers, and cloud-native services; monitoring and optimizing deployed AI systems; documenting reproducible development; and contributing to reusable standards and technical design reviews. The work supports defense and health agencies, with AI solutions intended to contribute to national security and public well-being. The role offers opportunities to shape agentic AI delivery practices, mentor teammates, and deepen expertise in advanced AI engineering. Salary range is $155,000–$200,000 annually, with actual compensation based on the selected candidate’s experience, education, certifications, skills, and qualifications.

Requirements

Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related field.
7+ years of hands-on software engineering experience, including significant experience delivering AI/ML, generative AI, or LLM-powered solutions in production or production-like environments.
Experience building generative AI applications and working with LLMs, tool/function calling, model evaluation, prompt/context engineering, or retrieval-augmented generation.
Experience designing or implementing agentic AI solutions using frameworks or platforms such as Agent2Agent Protocol, Bedrock Agents, Mastra, CrewAI, Strands, AgentCore, LangChain, or LangGraph.
Strong understanding of AI APIs and model platforms such as OpenAI, Anthropic, Gemini, Amazon Bedrock, and Google Vertex AI.
Proficiency in TypeScript and familiarity with Nx, Next.js, Mastra, Vercel AI SDK, Playwright, Jest, and Vite.
Python proficiency and familiarity with uv, Pydantic, FastAPI, CrewAI, LangChain, LangGraph, and Unstructured.
Curiosity and practical experimentation with emerging AI technologies, with a focus on responsible innovation and measurable mission impact.
Familiarity with CI/CD pipelines, including GitLab CI.
Experience with VS Code and AI extensions such as Cline and Claude Code.
Strong communication skills and a client-oriented mindset.
U.S. citizenship and eligibility for a DoD Secret clearance.

Nice To Haves

Exposure to adjacent skillsets such as data engineering, data science, UI/UX, cloud engineering, and platform engineering.
Experience with infrastructure as code tools such as Terraform, Open Tofu, AWS CDK, or CloudFormation for deploying cloud-native applications.
Knowledge of federal cybersecurity, RMF, FedRAMP, or regulatory frameworks.
Curiosity and experimentation with recent AI innovations, oriented toward delivering mission impact.

Responsibilities

Lead the design, development, prototyping, and refinement of AI-powered capabilities using generative AI, LLMs, retrieval patterns, prompt/context engineering, and secure API integrations.
Own end-to-end agentic AI development lifecycles, including model selection, agent design, tool/function calling, orchestration, response synthesis, evaluation, and deployment.
Design maintainable, testable, and scalable systems; conduct code reviews, establish technical patterns, and use AI-assisted development tools responsibly.
Collaborate with data engineers, software engineers, data scientists, cloud engineers, and mission stakeholders to translate business and mission needs into operational AI and automation solutions.
Design and automate deployment workflows using infrastructure as code, CI/CD pipelines, containers, and cloud-native services.
Monitor AI systems after deployment, tune performance, and apply reliability and scalability best practices.
Write clean, well-documented code following industry and federal guidelines, and support reproducible development.
Keep current with AI/ML trends, agentic frameworks, model capabilities, and engineering practices; mentor team members and contribute to reusable standards, documentation, and technical design reviews.