Svitla - Golang Developer - Microservices Architecture

About the Role : Senior Go Developer to design, build, and operate high-performance Go-based microservices, deployed on Kubernetes, and integrate AI/ML models into production systems in collaboration with Data Science/ML teams.Key Responsibilities : Design & Development : - Architect, design, and implement microservices in Go following best practices (clean code, SOLID principles, hexagonal/clean architecture when appropriate).- Build robust, scalable, and secure APIs (REST/gRPC) for internal and external consumers.- Collaborate with Data Scientists/ML Engineers to integrate AI/ML models into production services (model serving, feature pipelines, inference endpoints).- Write high-quality, testable, and maintainable code, including unit, integration, and contract tests.Microservices & System Architecture : - Decompose monoliths or larger systems into well-bounded microservices with clear ownership and interfaces.- Design and implement inter-service communication (gRPC, REST, messaging/streaming) with an eye on latency, reliability, and observability.- Contribute to the overall system architecture, including data flows, API design, and integration with external systems.Kubernetes & Cloud-Native Operations : - Containerize applications (Docker or similar) and define Kubernetes manifests/Helm charts for deploying services.- Collaborate with the DevOps/SRE team to ensure services are resilient, observable, and autoscalable in Kubernetes.- Implement and maintain monitoring, alerting, and logging (e.g., Prometheus, Grafana, OpenTelemetry, ELK, etc.)- Participate in performance tuning, load testing, and capacity planning for production workloads.AI/ML Integration : - Work with ML teams to operationalize models : - Model inference services- Feature preprocessing in real-time- Batch/offline inference pipelines- Ensure latency, scalability, and reliability of AI/ML-driven microservices.- Help define and implement A/B testing, canary deployments, and monitoring for model performance and drift (in collaboration with ML team).Technical Leadership & Collaboration : - Act as a technical mentor to mid-level and junior developers (code reviews, pair programming, design reviews).- Contribute to and help enforce engineering standards, coding guidelines, and best practices.- Participate in architecture and technical roadmap discussions, providing input based on hands-on experience.- Collaborate closely with Product Managers to refine requirements into technical solutions and clear delivery plans.Quality, Security & Reliability : - Champion test-driven development (TDD) and/or robust testing strategies.- Ensure solutions adhere to security best practices (authentication, authorization, data protection, secrets management).- Contribute to incident response and post-mortems, and drive improvements to prevent recurrence.Technical Skills : - 5+ years of professional software development experience, with 3+ years focused on Go (Golang).- Goroutines and channels (concurrency patterns)- Context usage and cancellation- Error handling patterns- Modules and dependency management- Experience building high-performance backend services and APIs (REST, gRPC).Microservices & Distributed Systems : - Hands-on experience designing and implementing microservice architectures.- Understanding of service discovery, API gateways, load balancing, and fault tolerance patterns (circuit breakers, retries, backoff, rate limiting).- Solid grasp of distributed systems concepts : eventual consistency, idempotency, transactions across services, etc.Kubernetes & Cloud : - Practical experience deploying, running, and debugging applications on Kubernetes (e.g., GKE, EKS, AKS, or self-managed clusters).- Familiarity with : - Writing/understanding Kubernetes manifests (Deployments, Services, ConfigMaps, Ingress, etc.)- Containerization with Docker or equivalent- CI/CD pipelines for building and deploying Go services- Experience with at least one major cloud provider (AWS, GCP, Azure, etc.)AI/ML Concepts (for Integration) : - Understanding of basic ML concepts : training vs. inference, features, model versions, evaluation metrics.- Experience integrating machine learning models into applications or microservices (using frameworks, REST endpoints, or libraries).- Familiarity with patterns like- Online/real-time inference (You dont need to build models from scratch, but you should be able to collaborate effectively with ML teams and design production-ready integration.)- Batch inference- Feature engineering pipelinesData & Storage : - Strong experience with relational databases (e.g., PostgreSQL, MySQL) and SQL.- Familiarity with NoSQL/datastores (e.g., Redis, MongoDB, DynamoDB, etc.)- Understanding of caching, message queues/streaming (e.g., Kafka, RabbitMQ, NATS, etc.)Testing, Observability & Tooling : - Experience with testing in Go (testing package, mocks, integration tests).- Familiarity with observability tools : metrics, logging, tracing (e.g., Prometheus, Grafana, Jaeger, OpenTelemetry).- Experience using Git and standard development workflows (code review, branching strategies).Soft Skills : - Strong communication skills : able to explain complex technical topics to both technical and non- technical stakeholders.- Ownership mindset : you care about the entire lifecycle of the service from design and implementation to deployment and monitoring.- Mentorship and leadership capabilities : willing to support, guide, and challenge peers constructively.- Comfortable working in an agile environment (Scrum/Kanban), with iterative delivery and continuous improvement.- Problem-solving and critical thinking : you enjoy digging into complex issues and finding pragmatic solutions (ref:hirist.tech)