Staff Engineer (Core & MLOps)

Accountabilities: • Architect and evolve the control and context planes, advancing service and schema registries, SLO enforcement, health-aware routing, automated canary releases, and operational feedback loops.

• Own the service chassis and golden path, maintaining and improving multi-language Java and Python client libraries, standardized workload specifications, Helm charts, and deployment pipelines.

• Define and govern inter-service contracts, including gRPC and Protocol Buffer definitions, API gateway transcoding, versioning policies, and schema evolution standards.

• Operate and improve the core platform infrastructure across Kubernetes, Terraform, HAProxy/Nginx, Confluent Kafka, real-time billing pipelines, Valkey, and database modernization initiatives.

• Lead architectural strategy through Requests for Discussion (RFDs) covering workflow orchestration, gateway orchestration, multi-cluster routing, automated failover, and other critical platform initiatives.

• Establish reliability engineering practices, including SLOs, SLIs, error budgets, fault isolation, and automated weighted canary deployments.

• Participate in shared infrastructure on-call rotations, lead incident post-mortems, and convert operational insights into platform improvements.

• Mentor engineers across multiple squads, review architectural proposals, and establish engineering practices that make reliable software development more consistent and efficient.

Requirements:

• 10+ years of experience building scalable distributed backend systems, with a strong track record of creating internal platforms or core libraries adopted across engineering organizations.

• Advanced Java expertise, including reactive frameworks such as Vert.x or Netty, combined with strong Python proficiency.

• Deep experience with gRPC and Protocol Buffers, including schema evolution and backward compatibility in mission-critical systems.

• Hands-on production experience with Kubernetes at scale, Terraform, and event-streaming platforms such as Kafka.

• Experience designing automated telemetry pipelines, materialized views, feature stores, or other feedback systems that use production data to dynamically improve system behavior.

• Strong reliability engineering background, including SLO/SLI definition, blast-radius analysis, fault tolerance, and rigorous service contracts.

• Exceptional technical writing skills and the ability to communicate complex architectural concepts clearly while driving alignment across teams.

• Strong written and interpersonal communication skills suited to a globally distributed, remote-first environment.

• A curious, continuous-learning mindset with an interest in evaluating new technologies, architectures, and engineering approaches.

• Experience with Temporal, DBOS, or similar durable execution platforms is a plus.

• MLOps experience, including model serving, performance monitoring, or production drift detection, is advantageous.

• Familiarity with zero-trust networking and service meshes such as SPIRE, mTLS, Cilium, Istio, or Envoy is beneficial.

• Experience building developer tooling such as CLIs, SDKs, or project generators is a plus.

• Experience with large-scale web scraping or crawling, or contributions to distributed-systems and data-extraction open-source projects, is advantageous.

Benefits:

• Fully remote, remote-first working environment with flexible working hours.

• Freedom and flexibility to work from the location where you are most productive.

• Opportunity to work on core infrastructure supporting large-scale web data pipelines and distributed systems.

• Exposure to cutting-edge open-source technologies, tools, and evolving AI and web data infrastructure.

• Opportunities to attend conferences and connect with colleagues across the globe.

• Collaboration with a diverse, multicultural, and globally distributed engineering community.

• High level of autonomy and organizational trust.

• Opportunities to influence platform architecture, engineering standards, and technical strategy across multiple teams.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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