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 Responsibilities

1. 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 model

performance 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-on

experience.

• Collaborate closely with Product Managers to refine requirements into technical solutions and

clear 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 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).

Job Description

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

• Batch inference

• Feature engineering pipelines

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.

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.