Sr Software Engineer - AI-First Development

Position Overview

The primary responsibility of the Senior Software Engineer (AI-First Development) is to design, orchestrate, and validate software applications built through AI-driven development workflows. This is not an AI-assisted traditional developer role. Rather than writing the majority of code by hand, this role operates within an AI-First Software Development Lifecycle (SDLC) where AI agents serve as the primary producers of code, configuration, and test artifacts.

Agent Workflow Design and Orchestration

• Design, build, and maintain AI agent workflows that produce application code, infrastructure configuration, test suites, and documentation.
• Select and configure appropriate AI models, agent frameworks, and tooling for each workflow based on task complexity, risk level, and cost considerations.
• Construct and maintain context stores that provide agents with organizational knowledge, coding standards, architectural patterns, and domain context needed to produce correct and consistent outputs.
• Apply compound engineering practices that systematically capture insights, patterns, and failure modes from each development cycle, encoding them into project memory, skills, and agent configurations so that each unit of work makes subsequent work easier and more reliable.
• Review, test, and approve AI-generated code, ensuring it meets Sands coding standards, architectural guidelines, and security requirements before promotion to production.
• Evaluate emerging AI models, agent frameworks, MCP servers, and development tools to continuously improve workflow effectiveness and output quality.

Verification and Quality Assurance

• Establish and enforce human-in-the-loop (HITL), on-the-loop (OHOTL), and after-the-loop (AHOTL) governance checkpoints appropriate to the risk level of each workflow.
• Identify and remediate patterns of agent drift, hallucination, or quality degradation across repeated workflow executions.
• Implement agent observability and telemetry systems that track agent behavior, tool call patterns, token consumption, and output quality metrics across workflows.

Application Development and Architecture

• Architect and deliver full-stack applications across web, API, and data layers using AI-First methodologies as the primary development approach.
• Collaborate with cross-functional teams including product, design, infrastructure, and security to translate business requirements into executable agent workflows.
• Maintain the ability to write, debug, and refactor code directly when agent outputs require manual intervention or when exploring novel architectural approaches.
• Ensure all delivered applications meet enterprise standards for scalability, maintainability, observability, and operational readiness.

Continuous Improvement and Mentorship

• Evaluate emerging AI models, agent frameworks, MCP servers, and development tools to continuously improve workflow effectiveness and output quality.
• Mentor team members on AI-First development practices, context engineering techniques, and verification methodologies.
• Contribute to the evolution of the Sands AI-First SDLC standard, proposing refinements based on practical experience and measurable outcomes.
• Document agent workflow patterns, prompt libraries, context store structures, and lessons learned to build institutional knowledge.

Minimum Qualifications

• At least 21 years of age.
• Proof of authorization to work in the United States.
• Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.
• 7+ years of professional software development experience, with demonstrated progression into senior or lead roles.
• 1+ years of hands-on experience using AI-assisted development tools (such as GitHub Copilot, Cursor, Claude Code, Windsurf, or similar) as a core part of the daily development workflow.
• Strong foundational knowledge across at least two major programming ecosystems (for example, .NET/C#, JavaScript/TypeScript, Python, Java, Go), with the ability to evaluate and validate AI-generated code in any language relevant to a given project.
• Experience with cloud platforms (Azure preferred, AWS or GCP also acceptable), including deployment, configuration, and cost management.
• Experience with containerization (Docker) and container orchestration (Kubernetes or similar).
• Demonstrated ability to conduct thorough code reviews, identify defects in AI-generated outputs, and provide constructive technical feedback.
• Excellent written and verbal communication skills, with the ability to articulate technical decisions and trade-offs to both technical and non-technical stakeholders.
• Strong interpersonal skills with the ability to communicate effectively and interact appropriately with management, other Team Members and outside contacts of different backgrounds and levels of experience.

Preferred Qualifications

• Experience designing multi-agent and subagent architectures using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom orchestration layers.
• Understanding of agent planning, tool use, memory, multi-step reasoning, and scoped tool access patterns.
• Practical experience constructing structured context packages for LLMs, including prompt design, RAG pipelines, context window optimization, project memory files (CLAUDE.md, AGENTS.md), and integration with MCP servers.
• Understanding of tactical context management strategies such as plan mode, context editing, and multi-session splitting.
• Experience authoring Skills (SKILL.md), configuring hooks for deterministic automation, building custom MCP servers, and assembling agent toolchains that enable repeatable, production-grade workflows.
• Experience implementing human-in-the-loop oversight models, automated evaluation pipelines, and strategies for detecting agent drift or hallucination.
• Familiarity with agent telemetry, token consumption monitoring, and cost governance across multi-agent workflows.
• Experience with microservices, event-driven architectures, or message-based systems (Kafka, RabbitMQ, Azure Service Bus).
• Understanding of enterprise integration patterns at scale.
• Knowledge of secure development practices, OWASP guidelines, and experience working within regulated industries (gaming, finance, hospitality, or similar).
• Understanding data privacy and responsible AI principles.
• Experience with unit testing, integration testing, end-to-end testing frameworks, and automated quality gates.
• Experience evaluating AI-generated test coverage and identifying gaps.
• Track record of mentoring developers, leading technical initiatives, and driving adoption of new development practices across teams.