Senior Go Developer

Role & responsibilities

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.
• Build and maintain scalable Go microservices and APIs.• Deploy, run, and optimize services on Kubernetes in the cloud.• Collaborate with ML teams to integrate and serve AI/ML models in production.• Requires strong Go, distributed systems, Kubernetes, and cloud experience, plus solid testing,observability, and communication skills.Key Responsibilities1. 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.2. 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.3. 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 auto scalable in Kubernetes.• Implement and maintain monitoring, alerting, and logging (e.g., Prometheus, Grafana, Open Telemetry, ELK, etc.).• Participate in performance tuning, load testing, and capacity planning for production workloads.4. AI/ML Integration(Note: You dont have to be a Data Scientist, but you should understand how ML systems work in production.)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 modelperformance and drift (in collaboration with ML team). 5. 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-onexperience.• Collaborate closely with Product Managers to refine requirements into technical solutions andclear delivery plans.6. 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 preventrecurrence.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, transactionsacross services, etc.• Kubernetes & Cloud• Practical experience deploying, running, and debugging applications on Kubernetes (e.g., GKE,EKS, AKS, or self-managed clusters).Job DescriptionFamiliarity 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, evaluationmetrics.• Experience integrating machine learning models into applications or microservices (usingframeworks, REST endpoints, or libraries). Familiarity with patterns like:• Online/real-time inference• Batch inference• Feature engineering pipelinesYou don’t need to build models from scratch, but you should be able to collaborate effectively with ML teams and design production-ready integration.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.