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 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