Java Machine Learning Jobs in New Jersey (NOW HIRING)

Job responsibilities 

• Architects and implements low-latency, high-throughput Java Spring Boot based distributed services, using object-oriented principles, that meet the performance demands of production-grade services with strong well-defined APIs 

• Designs and builds resilient, cloud-native service architectures with strong high-availability (HA) requirements, from 3 to 5 nines, leveraging standard AWS compute, messaging, streaming, DB and storage services like MSK (Kafka), SQS, S3, ECS, EKS, Lambda, KVS/KDS, RDS, Dynamo, Redshift, and S3. 

• Develops and maintains infrastructure-as-code solutions using Terraform and/or CloudFormation to support scalable, repeatable, and auditable cloud deployments 

• Implements and continuously improves observability solutions - including alerting, monitoring, and reporting - using Datadog, Dynatrace, and Splunk to deliver actionable production intelligence across microservices platforms 

• Translates ambiguous or evolving requirements into stable, well-modeled service designs, clearly articulating engineering tradeoffs to both technical and non-technical stakeholders 

• Leads technical design reviews, establishes engineering best practices, and drive adoption of standards that improve platform operability, reliability, and maintainability 

• Owns production outcomes end-to-end - identifying and resolving performance bottlenecks, reliability gaps, and scalability constraints through automation and runbook-driven operations 

• Partners with machine learning engineers and data scientists to understand platform requirements and deliver robust, production-ready engineering solutions 

• Mentors and provides technical guidance to engineers across the team, fostering a culture of ownership, continuous learning, and engineering excellence 

• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. 

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale. 

Required qualifications, capabilities, and skills 

• Formal training or certification on software engineering concepts and 5+ years' applied experience; very strong Java development skills using object-oriented principles, with strong experience using Spring Boot 

• Demonstrated experience designing and tuning for low-latency processing in production distributed systems 

• Hands-on experience leveraging standard AWS compute, messaging, streaming, DB and storage services like MSK (Kafka), SQS, S3, ECS, EKS, Lambda, KVS/KDS, RDS, Dynamo, Redshift, and S3 in large-scale, resilient service architectures 

• Practical experience implementing alerting, monitoring, and reporting solutions using Datadog, Dynatrace, and/or Splunk in production-grade environments 

• Strong engineering fundamentals including API design, testing discipline, and debugging in production contexts 

• Strong ability in one or more modern programming languages (e.g., Java, Python, Go, Rust) with heavy emphasis on Java, writing clean, maintainable, OO, and testable code 

• Develops and maintains infrastructure-as-code solutions using Terraform and/or CloudFormation to support scalable, repeatable, and auditable cloud deployments 

• Strong experience with containerization and orchestration technologies, including Docker and Kubernetes 

• Demonstrated ability to communicate engineering tradeoffs clearly to both technical and non-technical stakeholders 

• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls. 

Preferred qualifications, capabilities, and skills 

• Deep familiarity with low-latency, highly transactional architectures and advanced usage of AWS managed services (KVS/KDS) - particularly for real-time processing, distributed event handling, and efficient data storage and retrieval 

• Expertise designing and automating observability and reporting workflows using Datadog, Dynatrace, and Splunk to deliver actionable monitoring and production intelligence across microservices platforms 

• Experience with modern delivery practices including continuous integration and delivery, infrastructure-as-code, and containerized deployments that support reliable service delivery at scale 

• Experience with Terraform and/or CloudFormation for building and maintaining cloud infrastructure in an enterprise environment 

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.