Design and deliver production patterns for AI/ML capabilities (batch + real-time inference, feature pipelines, evaluation, monitoring).
Partner with Data Science teams to move models from experimentation to reliable, scalable, observable services.
Implement responsible AI controls (traceability, testing/evaluation, approvals, and appropriate human oversight where required).
Define and deliver NFRs and operational excellence (availability, latency, throughput, scalability, resiliency, RTO/RPO, capacity planning, observability/SLOs/runbooks) with secure-by-design and SDLC/audit compliance.
Required qualifications, capabilities and skills
Formal training or certification on software engineering concepts and 9+ years applied experience
Strong hands-on experience designing and delivering enterprise solutions end-to-end.
Java 11/17+ expertise, including Spring Boot microservices, API design, and testing practices.
Python 3.x expertise for services, automation, and data/ML integration.
Proven distributed systems experience: microservices, event-driven architecture, and messaging/streaming (Kafka or equivalent).
Strong data architecture fundamentals: relational/NoSQL patterns, caching (e.g., Redis), data consistency, and schema evolution.
Practical experience productionizing AI/ML: inference patterns, model packaging/serving, and monitoring/drift fundamentals.
Working knowledge of MLOps concepts and operational model lifecycle management.
DevOps delivery mindset: CI/CD, automated testing (unit/integration/contract), and release/rollback strategies.
Strong security and resiliency mindset suitable for regulated environments. Excellent communication and influence skills—able to explain trade-offs and align stakeholders.
Preferred qualifications, capabilities and skills
Kubernetes/container platforms and cloud-native patterns (autoscaling, config/secrets, service-to-service security).
GenAI experience (LLMs, RAG, vector search, evaluation frameworks, prompt/model governance) where applicable.
Modernization experience (monolith decomposition, strangler patterns, incremental migration).
Advanced production readiness/observability practices (SRE-style monitoring, readiness reviews, operational rigor).A
