Technology AI Architect

Job Title: Mid-Level Engineer - AIOps / MLops / Telemetry
Duration: 3+ Months
Location: Englewood, CO 80111 [Hybrid]

Overview:
We are seeking a Mid-Level Engineer with strong hands-on expertise in AIOps, MLOps, and Telemetry to support intelligent automation, observability, and operational excellence across enterprise systems. The ideal candidate will have 5+ years of experience in building, deploying, and managing AI/ML-driven operational platforms, with a strong focus on monitoring, automation, and system reliability.
This role requires a highly analytical professional who can design scalable solutions, integrate telemetry pipelines, and enhance system performance using machine learning-driven insights.

Required Skills & Experience:
AIOps - 5+ Years
Design and implement AIOps solutions to automate IT operations and incident management.
Apply machine learning techniques for anomaly detection, root cause analysis, and predictive analytics.
Integrate AIOps platforms with monitoring and alerting tools.
Improve MTTR through intelligent event correlation and automation.

MLOps - 5+ Years
Build and maintain scalable ML pipelines for training, validation, deployment, and monitoring.
Manage CI/CD workflows for ML models.
Implement model versioning, monitoring, and performance tracking.
Collaborate with data scientists to operationalize models in production environments.
Ensure governance, reproducibility, and lifecycle management of ML models.

Telemetry - 5+ Years
Design and manage telemetry data pipelines (metrics, logs, traces).
Implement observability frameworks using industry-standard tools.
Analyze telemetry data to improve performance, availability, and scalability.
Establish monitoring dashboards and alerting mechanisms.
Optimize system instrumentation and data collection strategies.

Key Responsibilities:
Develop and maintain intelligent automation systems leveraging AIOps principles.
Build robust MLops pipelines to operationalize machine learning models.
Design telemetry frameworks to ensure deep observability across distributed systems.
Drive continuous performance optimization through data-driven insights.
Collaborate with DevOps, SRE, Data Engineering, and application teams.
Troubleshoot complex production issues using telemetry and ML-driven diagnostics.
Implement best practices for reliability, scalability, and security.
Document architecture, processes, and operational standards.

Preferred Qualifications:
Experience with cloud platforms (AWS, Azure, or GCP).
Familiarity with containerization and orchestration (Docker, Kubernetes).
Experience with monitoring/observability tools (Prometheus, Grafana, ELK, Datadog, Splunk, etc.).
Knowledge of Python or similar programming languages for ML workflows.
Strong understanding of CI/CD and DevOps practices.
Exposure to SRE principles and reliability engineering