Lead Software Engineer

Write production-quality code regularly across services, APIs, workflow engines, and AI-enabled capabilities

Lead delivery of high-impact features from design through deployment and production support

Modernize legacy components incrementally using pragmatic, low-risk migration patterns

Design, build, and evolve scalable microservices, APIs, integrations, and event-driven workflows

Translate business and product requirements into reliable software designs, implementation plans, and working solutions

Identify implementation risks early and drive practical solutions that keep delivery moving without sacrificing quality

Design and implement AI-enabled workflows using large language models, retrieval patterns, structured outputs, and tool/function calling

Build agentic systems that can execute multi-step workflows, invoke tools, call APIs, maintain state, and support human-in-the-loop reviews where appropriate

Implement MCP-style or equivalent orchestration patterns for context management, tool access, memory, permissions, and workflow execution

Integrate AI workflows with enterprise data sources, business applications, APIs, queues, and operational systems

Apply grounding, validation, guardrails, prompt/version management, and evaluation techniques to reduce hallucinations and improve reliability

Build monitoring, observability, feedback loops, and quality checks for production AI behavior

Partner with product, security, architecture, and operations teams to ensure AI solutions meet business, privacy, compliance, and supportability requirements

Lead technical execution for a team, feature area, or workstream while remaining hands-on in the codebase

Contribute to architecture decisions for service boundaries, integration patterns, data ownership, and AI workflow design

Make sound engineering tradeoffs across delivery speed, reliability, scalability, security, cost, and maintainability

Serve as a technical escalation point for implementation challenges involving distributed systems, AI workflows, data integrations, and production issues

Create reusable patterns, examples, and guidance that help engineers deliver consistent, high-quality solutions

Use AI-powered development tools such as GitHub Copilot, ChatGPT, Claude, or equivalent tools to accelerate coding, refactoring, testing, debugging, and documentation

Establish practical team practices for safe, reviewable, and high-quality AI-assisted development

Create prompts, reusable workflows, coding patterns, and automation scripts that improve developer productivity

Help engineers adopt AI-driven development without weakening code review discipline, testing standards, security practices, or production ownership

Mentor engineers through pairing, code reviews, design discussions, and implementation planning

Help engineers build stronger judgment around AI-enabled development, workflow automation, testing, and production readiness

Coach less-experienced developers on modern engineering practices, secure AI usage, and maintainable system design

Promote a culture of ownership, learning, collaboration, and technical accountability

Lead by example with clear communication, humility, urgency, and high engineering standards

Build and maintain SaaS applications using modern frameworks and cloud platforms

Design and implement RESTful APIs and event-driven integrations

Work with relational and NoSQL databases, optimizing for performance and reliability

Build containerized applications using Docker and deploy via Kubernetes

Partner with DevOps/SRE to ensure strong CI/CD pipelines, observability, and safe deployments

Participate fully in the SDLC: design, coding, testing, deployment, and production support

7+ years of professional software development experience

Proven experience leading delivery of complex features, services, or technical workstreams

Hands-on experience designing, building, and operating production software systems

Experience applying AI-assisted development tools in real engineering workflows

Hands-on exposure to LLM-enabled applications, agentic workflows, automation systems, or AI-integrated product capabilities

Demonstrated ability to mentor engineers and improve team execution through practical technical leadership

Experience with Python, Java, TypeScript, or polyglot engineering environments

Experience with message queues, event streaming, or workflow orchestration platforms

Experience with vector databases, retrieval-augmented generation, knowledge graphs, or semantic search

Experience building AI evaluation harnesses, prompt/version management, safety checks, or governance workflows

Frontend framework experience such as React, Angular, or Vue

Background with high-throughput, real-time, or operationally critical SaaS systems

Strong background in Agile/Scrum environments and cross-functional delivery

Bachelor’s degree in computer science or equivalent practical experience

You consistently deliver high-quality features, services, and AI-enabled workflow capabilities

Agentic AI workflows you build are reliable, observable, secure, and useful in real business processes

The team moves faster because AI-assisted development practices are applied safely and effectively

Legacy components are improved incrementally without creating unnecessary delivery risk

Engineers rely on you for implementation guidance, design judgment, and practical problem solving

Code quality, test coverage, operational readiness, and delivery predictability improve measurably

You are recognized as a hands-on technical leader who ships, mentors, and raises the team’s engineering bar