Ai Engineer Jobs

About The Position

Requirements

• Significant hands-on software development experience delivering production-quality applications.
• Experience leading a team of developers, including mentoring, code review, technical planning, delivery guidance, and issue resolution.
• Experience setting technical direction for software delivery projects, including architecture decisions, engineering standards, delivery patterns, and technical risk management.
• Experience with at least one modern programming language such as Java, Python, JavaScript/TypeScript, C#, Go, or comparable technologies.
• Experience with either new application development, application modernization, or both.
• Practical experience using AI-assisted development tools to support coding, refactoring, testing, documentation, debugging, code comprehension, and architecture exploration.
• Experience reviewing and validating AI-generated code for correctness, security, maintainability, performance, testability, and alignment with architecture and requirements.
• Familiarity with agentic development concepts and experience building or prototyping fit-for-purpose agents using LangGraph, A2A, MCP-enabled tools, or similar frameworks.
• Strong understanding of software architecture and engineering fundamentals, including APIs, data models, integration patterns, automated testing, version control, CI/CD, secure coding, observability, and operational support.
• Ability to analyze existing codebases, identify modernization opportunities, define target-state architecture, and create pragmatic incremental transformation plans.
• Ability to communicate technical direction clearly to developers, architects, stakeholders, and client teams.
• Growth mindset and enthusiasm for how AI will reshape the software development profession.

Nice To Haves

• Experience modernizing legacy applications, including mainframe, Fortran, J2EE, ASP, monolithic, or other enterprise application estates.
• Experience architecting cloud-native applications, APIs, microservices, event-driven systems, or modern front-end experiences.
• Hands-on experience with LangGraph, A2A, MCP, OpenAI or Anthropic APIs, Semantic Kernel, CrewAI, or comparable agent and orchestration frameworks.
• Experience designing agents that interact with enterprise tools, repositories, documentation, tickets, CI/CD pipelines, runtime telemetry, or application data.
• Experience defining architecture guardrails, reference implementations, coding standards, reusable components, or engineering playbooks for delivery teams.
• Familiarity with DevSecOps practices, automated quality gates, observability, containerization, infrastructure as code, and secure software delivery pipelines.
• Experience with retrieval-augmented generation, vector databases, tool calling, evaluation harnesses, prompt engineering, LLM application testing, or agent observability.
• Experience working in Agile delivery environments with product owners, architects, business stakeholders, client executives, and distributed engineering teams.
• Ability to mentor and influence other developers in practical, responsible, and effective use of AI-assisted software development practices.

Responsibilities

• Set technical direction for delivery teams, including architecture, engineering practices, modernization approach, AI adoption patterns, and quality expectations.
• Remain hands-on in the work by designing, coding, reviewing, testing, and troubleshooting software while helping other developers deliver well-architected solutions.
• Use AI-assisted development tools and create fit-for-purpose agents and agentic workflows using frameworks such as LangGraph, A2A, MCP-enabled tools, or similar approaches when they are the right solution for a client or delivery challenge.
• Lead a team of developers through new application builds and modernization programs while remaining actively involved in design, coding, review, testing, and troubleshooting.
• Set technical direction for projects, including architecture, integration patterns, modernization strategy, engineering standards, delivery approach, and technical risk management.
• Use AI-assisted development tools to accelerate code comprehension, generation, refactoring, testing, documentation, and migration planning while ensuring that outputs are validated by experienced engineering judgment.
• Architect and build fit-for-purpose agents and agentic workflows using frameworks such as LangGraph, A2A, MCP-enabled tools, or comparable technologies.
• Guide modernization of existing applications by analyzing legacy code, identifying business logic, assessing constraints, defining target architecture, and creating incremental transformation roadmaps.
• Make pragmatic architecture decisions across APIs, data models, cloud services, security, observability, integration, DevSecOps, and user experience considerations.
• Mentor developers in software engineering practices, AI-assisted delivery techniques, secure coding, automated testing, maintainable design, and effective code review.
• Collaborate with architects, product owners, business stakeholders, client technical teams, and delivery leaders to translate business needs into executable technical plans.
• Identify and manage risks in AI-generated software, including brittle code, hidden assumptions, weak tests, security issues, licensing concerns, maintainability gaps, and architecture drift.
• Create reusable patterns, accelerators, prompts, agents, reference architectures, and engineering practices that help the broader team deliver modernization work more effectively.