Go Developer

Job Description 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 don't 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 pipelines - Data & 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.