Go Programming Jobs in Colorado (NOW HIRING)

Job Overview:

Pay Range: $67hr - $72hr

Requirement/Must Have:

• 12+ years of experience in software engineering, demonstrated experience building and operating production-critical backend systems with meaningful business or revenue impact.
• Strong hands-on experience with Go/Golang or Java based technologies in real-world production environments.
• Experience integrating AI/ML solutions into enterprise systems, including deploying, consuming, or operationalizing ML models.
• Understanding of AI/ML concepts such as supervised/unsupervised learning, model evaluation, inference pipelines, and data quality considerations.
• Experience applying AI/ML within SDLC processes (e.g., testing, monitoring, observability, or developer productivity tooling).
• Deep experience with relational databases (e.g., MySQL, PostgreSQL, Oracle), including schema design, transactional modeling, and performance optimization.
• Strong understanding of transactional systems, including data integrity, idempotency, reconciliation, auditability, and failure handling.
• Proven ability to make pragmatic, low-complexity design decisions in distributed systems.
• Experience working within and improving existing systems with real-world constraints, rather than primarily greenfield environments.
• Ability to independently identify problems, define solutions, and drive execution to completion.
• Strong communication skills, including the ability to work effectively with both technical and non-technical stakeholders.

Responsibilities:

• Own and evolve existing Go-based services, making measured, low-risk changes that improve system capability, reliability, and clarity.
• Design and implement backend functionality for payment processing, transaction routing, reconciliation, and financial data movement with a strong focus on correctness and auditability.
• Integrate AI/ML components into the existing architecture (e.g., model inference services, decision engines, data pipelines) while ensuring system stability and auditability.
• Apply AI/ML across the SDLC, including intelligent test generation and validation, production anomaly detection and incident prediction, log and metric analysis using ML techniques, and code quality and defect prediction tools.
• Collaborate with data science teams to operationalize ML models (deployment, monitoring, versioning, rollback strategies) within a production-grade environment.
• Proactively identify system limitations, operational risks, and data gaps, and drive pragmatic solutions with minimal oversight.
• Evaluate the impact and risk of changes in a high-throughput transactional environment, ensuring safe and controlled rollouts.
• Improve system observability, data capture, and operational insight to support both engineering and business needs, including ML-driven observability enhancements.
• Model and optimize transactional data using relational databases, with careful attention to consistency, performance, and maintainability.
• Build and maintain APIs and service interfaces with clear contracts and long-term usability in mind.
• Contribute to AI transformation initiatives, identifying where AI/ML can meaningfully enhance system reliability, fraud detection, operational efficiency, or decision-making.
• Collaborate with product, architecture, and non-technical stakeholders to ensure solutions meet business, operational, regulatory, and ethical AI governance requirements.
• Provide technical leadership by guiding design decisions, reviewing code, and mentoring engineers while maintaining a focus on simplicity and stability.
• Own services through their full lifecycle, including design, deployment, monitoring, incident response, and continuous improvement, including ML lifecycle management (MLOps).

Nice to Have:

• Experience with payment systems, financial transaction platforms, or other high-integrity data domains.
• Experience supporting systems with high transaction volume, low latency requirements, or strict uptime expectations.
• Familiarity with MLOps practices, including model deployment, monitoring, drift detection, and lifecycle management.
• Experience with real-time or near real-time ML inference systems in production environments.
• Familiarity with secure data handling, privacy considerations, and ethical AI practices in regulated environments.
• Experience with containerized or cloud-based deployment environments (e.g., Kubernetes, Docker, or similar platforms).
• Exposure to event-driven or asynchronous processing patterns.

Qualification And Education:

• Demonstrates restraint and discipline in system design, avoiding unnecessary abstraction and complexity.
• Comfortable working in high-stakes environments where stability and correctness take precedence over novelty.
• Able to apply AI/ML pragmatically, focusing on measurable improvements rather than experimentation for its own sake.
• Calm, methodical, and reliable under production pressure.
• Able to collaborate effectively with senior peers while mentoring and guiding junior engineers.
• Focused on building systems that are understandable, maintainable, resilient, and progressively intelligent over time.