Senior AI Software Engineer

Credence•Boston, MA

4d•$155,000 - $200,000

About The Position

Credence is seeking a Senior AI Software Engineer to join its AI and Automation practice. The role is a senior technical contributor responsible for designing, developing, and deploying advanced AI-driven solutions, including generative AI, LLM-powered applications, and agentic AI systems. The engineer collaborates with engineering, data, cloud, and stakeholder teams to deliver scalable, secure, mission-focused capabilities for federal clients, while helping guide technical direction, strengthen engineering practices, and mentor other engineers. The position covers the full lifecycle of AI and agentic systems, from solution design and prototyping through integration, deployment, monitoring, and optimization. It also involves applying sound software engineering practices, integrating cloud and DevSecOps capabilities, documenting work to industry and federal guidelines, and contributing to technical standards and design reviews. The work supports defense and health agencies and is intended to advance national security and public well-being. The stated annual salary range is $155,000–$200,000. Compensation depends on the selected candidate's experience, education, certifications, skills, and overall qualifications.

Requirements

Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related field.
At least 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, working with LLMs, implementing tool/function calling, and applying model evaluation, prompt/context engineering, or retrieval-augmented generation approaches.
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 leading AI APIs and model platforms such as OpenAI, Anthropic, Gemini, Amazon Bedrock, and Google Vertex AI.
Proficiency in TypeScript and familiarity with libraries and frameworks such as Nx, Next.js, Mastra, Vercel AI SDK, Playwright, Jest, and Vite.
Proficiency in Python and familiarity with libraries and frameworks such as 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

Curiosity and experimentation with the latest AI innovations, oriented toward delivering mission impact.
Exposure to adjacent areas such as data engineering, data science, UI/UX, cloud engineering, and platform engineering.
Experience with Infrastructure as Code tools such as Terraform, OpenTofu, AWS CDK, or CloudFormation.
Knowledge of federal cybersecurity, RMF, FedRAMP, or regulatory frameworks.

Responsibilities

Lead the design, development, prototyping, and refinement of AI-powered capabilities using generative AI, LLMs, retrieval patterns, prompt and context engineering, and secure API integrations.
Own end-to-end agentic AI development lifecycles, including model selection, agent design, tool and function calling, orchestration, response synthesis, evaluation, and deployment.
Design maintainable, testable, and scalable systems; conduct code reviews, establish technical patterns, and responsibly use AI-assisted development tools.
Collaborate with data engineers, software engineers, data scientists, cloud engineers, and mission stakeholders to turn 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 deployed AI systems, tune performance, and apply reliability and scalability best practices.
Write clean, documented code in accordance with 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.