Senior Software Development Engineer / Technical Lead with AI Focus

Role Description

We are seeking a hands-on Senior Software Developer / Technical Lead with an AI focus to help us lead teams that build new applications and modernize existing ones for our clients. You will work as part of an AI Led Application Modernization team that believes the way software is designed, built, tested, and maintained is changing quickly—and that senior technical leaders have an opportunity to shape how teams adopt these changes responsibly and effectively. Among your responsibilities will be:

• 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.
• This role is both hands-on and leadership oriented. You will write code, review AI-generated output, guide architectural decisions, mentor developers, establish delivery patterns, and help teams use AI responsibly to build and modernize software faster without sacrificing quality, security, or maintainability.

Qualifications

• 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.

Requirements

• 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.