Establish and own engineering standards across the team (architecture patterns, coding standards, testing strategy, operational readiness).
Define and operate service reliability practices (SLIs/SLOs, incident response, root-cause analysis, postmortems, and continuous reliability improvements).
Identify recurring operational issues and automate remediation to improve system stability and observability (logs/metrics/tracing).
Build and maintain CI/CD pipelines and release strategies (quality gates, canary/blue-green, rollback/feature flags) to enable safe, predictable delivery.
Communicate status and risks clearly; manage priorities and dependencies across multiple initiatives; collaborate in a Scrum team and participate in Agile ceremonies.
Deliver production code primarily in Java (and Python as needed) using AWS (e.g., ECS) and related technologies.
Required qualifications, capabilities, and skills
Formal training or certification in Software Engineering and 5+ years of applied experience building production systems.
Strong system design, application development, and operational stability skills in production environments (including on-call/incident ownership).
Expertise designing and building microservices and RESTful APIs, with strong knowledge of data technologies and integration patterns.
Hands-on experience building cloud-native systems on at least one major cloud platform (AWS, GCP, or Azure).
Experience with distributed systems concepts (scalability, resiliency, concurrency, idempotency, failure modes, and performance tuning).
Proven ability to influence product design and technical direction by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment, with demonstrated ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Demonstrated experience leading effective team use of approved AI-assisted development practices (coding, code review, test acceleration, troubleshooting), including setting expectations and reusable validation patterns.
Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, summarization/extraction, workflow assistants), including evaluation and monitoring considerations.
Understanding of responsible AI use in engineering workflows (data sensitivity, safe handling of inputs/outputs, resiliency/security expectations) and ability to guide peers on safe and effective usage.
Strong communication and stakeholder management skills; ability to deliver across multiple initiatives and drive outcomes through ambiguity.
Preferred qualifications, capabilities, and skills
AWS cloud certification(s).
AI/ML or GenAI certification(s) (or equivalent practical experience delivering LLM-enabled capabilities).
Advanced Java proficiency (primary) and working knowledge of Python.
Experience with Docker, Kubernetes, and modern CI/CD practices.
Experience with multi-region deployments, disaster recovery, and zero-downtime release patterns.
Strong understanding of responsible AI practices, including prompt/agent safety patterns and secure handling of sensitive data.
Track record delivering scalable, reliable, and secure products from concept to launch in a regulated environment.
Domain familiarity with financial services technology; IVR/CCaaS experience a plus.
