SSE (Architect) - Java MLOps

Senior Software EngineerCommunications Intelligence & Platform SystemsWhat You’ll DoOperationalize and deploy machine learning models built by Data Science teams using robust MLOps practices , including CI/CD pipelines, model versioning, monitoring, and rollback strategies , Feature Engineering, orchestrate ETL processes, manage data processing pipelines , and automate machine learning workflowsManage model deployment lifecycles , ensuring production-grade reliability, scalability, and observability of model-driven services integrated into communication decisioning systems.Design and build large-scale communication and personalization platforms that deliver intelligent, event-driven notifications across channels (email, push, SMS, chat) with sub-second latency and enterprise-grade reliability.Architect distributed backend services and microservices for campaign orchestration, lead ingestion, customer personalization, and communication governance — ensuring extensibility, observability, and high throughput across millions of daily interactions.Build event-driven systems using Kafka and real-time data streams, powering automated messaging, feedback loops, and decisioning workflows.Develop and maintain scalable RESTful APIs and Spring Boot–based microservices, enabling integration across marketing automation systems, customer data pipelines, and external vendors.Implement orchestration and workflow frameworks that ensure robust communication between asynchronous systems — connecting campaign logic, customer events, and ML-based decision engines.Enhance platform reliability and observability by building monitoring dashboards, SLA enforcement metrics, and automated anomaly detection for throughput, latency, and deliverability.Collaborate cross-functionally with Product, Data Science, and Marketing to define requirements, ensure system interoperability, and drive measurable impact in engagement and conversion.Raise technical excellence by promoting best practices in backend architecture, distributed system design, API lifecycle management, and continuous integration/deployment.We Are a Match Because You HaveBachelor’s or Master’s degree in Computer Science, Software Engineering, or related technical discipline.10+ years of experience building high-scale backend platforms and APIs for data-intensive or event-driven systems.Mandatory: Deep expertise in MLOps — including model deployment, pipeline automation, monitoring, feature engineering , ETL frameworks and scaling of ML services in production.Experience deploying and maintaining ML or rule-based decision systems built by Data Science teamsStrong command of Java (Spring Boot) and working knowledge of Python or Go, with deep understanding of object-oriented design, concurrency, and multithreaded programming.Strong command of stream processing frameworks (Flink, Spark Streaming) or Pub/Sub architectures.Expertise in Kafka (topics, partitions, consumer groups, schema management) and event-driven microservices design.Proven experience developing RESTful APIs and microservices with Spring Boot, including authentication, routing, and observability integration.Strong background in cloud-native application design — deploying and managing workloads on GCP, AWS, or Azure, leveraging Docker and Kubernetes for containerization.Solid foundation in data modeling, SQL/NoSQL databases, and high-performance data access patterns.Strong problem-solving, debugging, and optimization skills — with an emphasis on scalability, fault tolerance, and SLA-driven reliability.Familiarity with observability tools (Datadog, Grafana) and distributed tracing for root-cause analysis.Ability to mentor engineers, perform design/code reviews, and lead technical discussions across multiple workstreams.Excellent communication and collaboration abilities across engineering, product, and analytics teams.Nice to HaveExperience with LLM or GenAI applications (e.g., prompt orchestration, ranking pipelines, or creative optimization systems).Exposure to campaign management, notification orchestration, or template-based content systems.Experience building multi-channel messaging systems (email, push, chat, SMS) with high concurrency and low latency.Knowledge of A/B testing infrastructure, experimentation frameworks, and message-governance pipelines.Background in config-driven workflow automation, feature flagging, or API gateway management.Success in this Role Looks LikeYou’ve delivered reliable, high-throughput services that handle millions of real-time events daily.You’ve deployed and managed production ML services through automated pipelines and robust observability.You’ve built robust Kafka-based pipelines that integrate seamlessly with downstream personalization or analytics systems.You’ve standardized Spring Boot API patterns and platform observability frameworks used across multiple teams.You’re viewed as a technical anchor — mentoring peers, driving platform reusability, and influencing architectural decisions.You proactively identify opportunities to reduce latency, improve reliability, and scale decisioning systems intelligently.