What are the responsibilities and job description for the AI Engineer (SDLC Automation/DevOps) position at Innovee Consulting LLC?
Job Title: AI Engineer (SDLC Automation/DevOps)
Duration: 8 Months
Location: Austin, TX
Responsibilities
Duration: 8 Months
Location: Austin, TX
Responsibilities
- Design and implement AI/ML models to enhance SDLC processes and developer productivity.
- Develop intelligent automation solutions for test management, infrastructure prediction, alert noise reduction, incident classification, and CI/CD optimization.
- Apply GenAI, RAG, MCP, and agentic AI techniques for internal automation use-cases.
- Build and maintain pipelines for training and improving AI models for DevOps scenarios.
- Identify automation opportunities by collaborating with Development, DevOps, Cloud, and Infrastructure teams.
- Develop scripts and tools to eliminate manual tasks and improve operational efficiency.
- Create technical architecture and design documentation for AI-driven solutions.
- Integrate AI capabilities into observability and monitoring platforms.
- Support system analysis, troubleshooting, performance tuning, and root cause diagnosis.
- Administer GitHub repositories, branching strategies, access control, governance rules, and CI/CD workflows.
- Use GitHub Actions or similar CI/CD tools for automation and workflow integration.
- Document technical designs, automation solutions, and processes.
- Contribute to Agile Scrum ceremonies and DevOps practices.
- 8 years of experience with Proven ability to administer GitHub Enterprise Cloud
- 8 years of experience with Proven ability to analyze and resolve complex issues
- 8 years of experience with Supporting and training end users on all levels.
- 8 years of experience with Hands-on experience with Continuous Integration Delivery models
- 3 years of experience with Hands-on experience with large development projects using Agile methodology
- Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
- 3 years of hands-on experience in Development or Automation engineering roles.
- Strong cloud-native knowledge (AWS, Azure, or GCP).
- Proficiency in scripting and automation (Python preferred; Bash acceptable).
- Solid understanding of CI/CD pipelines and DevOps workflows.
- Experience applying AI/ML techniques to engineering problems (classification, clustering, anomaly detection, prediction).
- Familiarity with ML frameworks: Scikit-learn, TensorFlow, PyTorch.
- Strong understanding of monitoring, logging, and observability systems.
- Experience with cloud-native technologies and DevOps tooling.
- Experience with anomaly detection, predictive analytics, and time-series forecasting.
- Knowledge of MLOps best practices for internal model development.
- Experience integrating AI solutions into DevOps pipelines/platforms.
- Familiarity with Infrastructure-as-Code tools (Terraform, Pulumi, CloudFormation).
- Experience with Hyper-V VM Management.
- Asset and service account management experience.
- Experience with BMC Helix or similar ITSM ticketing tools.