- Formal training or certification on software engineering concepts and 5+
years applied experience; Hands-on practical experience delivering system design, application development, testing, and operational stability
- The successful candidate demonstrates deep distributed-systems engineering
expertise in Python/Java plus strong platform, delivery, and production-operability discipline; Recent hands-on software development experience in large-scale distributed systems, primarily Python and modern microservices.
- Strong Python experience for AI/ML engineering, automation, model
operationalization, and agentic AI services, including tool integration, monitoring, telemetry, and governance; Strong experience with REST APIs and service-oriented / microservices architecture.
- Demonstrated experience leading effective use of approved AI-assisted
software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows,
including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Experience developing in Linux environments.
- Strong Kubernetes orchestration experience (building, deploying, and
operating production services).
- Messaging expertise with Kafka, MQ, or similar platforms.
- Experience with backend infrastructure patterns (e.g., load balancing,
autoscaling; Experience with NoSQL databases such as Cassandra; Experience with log analytics / observability tools (e.g., ELK, Splunk).
- Strong SDLC knowledge and agile ways of working, including CI/CD, application
resiliency, security, testing, and operational stability; Strong communication skills and proven ability to influence across senior technology and business stakeholders.
- AI/ML platform exposure (MLOps, feature engineering, model
hosting/operationalization; AWS and/or hybrid on-prem + cloud); Agentic AI experience: building and operating LLM-driven agents with tool integration, monitoring/telemetry, and governance/audit considerations.