As a Lead Software Engineer  , you will own the delivery of complex, AI-powered software initiatives from discovery through production with minimal supervision. You will design and build intelligent systems leveraging large language models and agentic approaches, expose capabilities through well-designed APIs and microservices, and operate confidently across a multi-cloud environment – ensuring portability, security, and reliability at every layer.

Job responsibilities

• Lead initiatives end-to-end – from requirements clarification and architecture through implementation, testing, release, and production support – with strong ownership and minimal supervision
• Design and implement AI solutions using large language models and modern agent patterns, including prompting strategies, tool/function calling, retrieval patterns, routing, and memory/state management where applicable
• Build guardrails, evaluation frameworks, monitoring pipelines, and cost/latency optimizations for production LLM-based systems
• Design, build, and operate REST and gRPC APIs and microservices, defining clear contracts using OpenAPI and Protobuf while ensuring backward compatibility, authentication, rate limiting, and observability
• Apply resilience engineering patterns – including timeouts, retries, and circuit breakers – to ensure reliable, production-grade service behavior
• Build and maintain well-tested, maintainable Python services and automation with clear packaging, dependency management, and architectural standards
• Own data design and implementation, including schema design, data access patterns, and complex SQL optimization aligned to performance and reliability requirements
• Build and manage infrastructure as code using Terraform, supporting containerized deployments via Kubernetes and CI/CD pipelines across multi-cloud environments
• Drive engineering excellence across code quality, testing strategy, performance, reliability, and operational rigor, including leading root-cause analysis for complex production issues
• Mentor engineers, provide technical guidance, and establish standards for delivery and engineering practices across the team
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automations.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and advanced applied experience
• Proven track record leading software delivery end-to-end with strong ownership and the ability to execute independently across the full development lifecycle
• Strong Python software engineering skills for building production-grade services and automation, with solid testing, packaging, and maintainability practices
• Strong understanding of relational databases and SQL, including schema design, query optimization, indexing, and transaction management
• Demonstrated experience building AI solutions using large language models in production environments, including quality assurance, safety controls, evaluation, observability, and cost management
• Strong API and microservices engineering experience, including service design, contract definition, security patterns, performance tuning, and distributed system observability
• Hands-on multi-cloud experience (AWS preferred) with strong distributed systems fundamentals and a portability-minded approach to design
• Strong Terraform skills for infrastructure-as-code, module design, environment management, and remote state handling
• Working knowledge of DevOps practices including CI/CD pipelines, Git-based workflows, and Kubernetes deployments
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practicests.

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