SSE (Architect) - Java ML Engineering

Senior Software EngineerCommunications Intelligence & Platform SystemsWhat You’ll Do- Operationalize 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 workflows - Manage 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 Have- Bachelor’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 teams - Strong 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 Have- Experience 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 Like- You’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.