Distinguished Software Engineer – AI​/ML Engineer

• Lead technical development of next-generation agentic AI systems and intelligent automation solutions for mission-critical reliability, scalability, and operational excellence
• Architect and implement machine learning platforms and autonomous agents for change management, monitoring, prediction, and automated issue resolution
• Design and implement multi-agent orchestration platforms for change management, capacity planning, and performance optimization
• Build intelligent observability and monitoring platforms using ML-driven anomaly detection, predictive analytics, and autonomous resolution
• Develop self-healing infrastructure platforms that predict, prevent, and automatically remediate system issues
• Design and build tools improving latency, availability, scalability, and change management
• Engineer reliability using metrics and measurements across domains
• Enable system scaling through technical solutions, automation, and process optimization
• Build failure-prevention tools and automation for mission-critical services
• Enhance instrumentation for cohesive end-to-end system health visibility
• Architect and implement fault-tolerant systems across hybrid cloud infrastructure
• Reduce MTTD and MTTR through intelligent automation and predictive capabilities
• Partner with service owners to define SLA breach detection and change-related anomaly handling
• Troubleshoot and analyze large-scale distributed systems
• Deliver autonomous reliability solutions using machine learning, NLP, and computer vision
• Drive development of MLOps and AIOps platforms for continuous learning, deployment, monitoring, and autonomous optimization
• Implement CI/CD pipelines with automated validation, deployment, rollback, and observability
• Build reusable reliability infrastructure, intelligent monitoring platforms, and developer productivity tools
• Provide technical mentorship and thought leadership through code reviews, design discussions, and knowledge sharing

• Bachelor’s degree in computer science, computer engineering, computer information systems, software engineering, or related area and 6 years’ experience in software engineering or related area; OR 8 years’ experience in software engineering or related area
• 12+ years of hands-on experience in Reliability Engineering, AI/ML Engineering, or Platform Engineering
• Proven record as a senior individual contributor influencing architecture and driving technical excellence across large organizations
• Deep experience operating mission-critical systems
• Expertise in MTTD, MTTR, availability, change management, model performance, and autonomous system reliability
• Expert-level AI/ML engineering experience, including Tensor Flow, PyTorch, and large-scale production ML deployments
• Advanced experience with agentic AI systems, multi-agent frameworks, autonomous decision-making systems, LLM-based agents, and agent orchestration platforms
• Comprehensive Reliability Engineering expertise, including Incident, Problem, and Change Management and performance and capacity engineering for AI/ML systems
• Expert-level cloud engineering experience with Azure, GCP, or AWS
• Experience with Kubernetes, Docker, serverless architectures, and cloud-native AI services
• Deep observability experience across distributed tracing, metrics, logs, APM, and AI-driven anomaly detection
• Strong platform engineering background including infrastructure as code, service mesh architectures, API gateways, and self-service developer platforms
• Preferred:
  Master’s degree and 4 years' experience in software engineering or related area
• Preferred: MLOps and model lifecycle management using MLflow, Kubeflow, or Seldon
• Preferred: NLP and computer vision expertise
• Preferred:
  Edge computing and distributed systems experience
• Preferred:
  Kafka or Pulsar
• Preferred:
  Chaos engineering, fault injection, and performance optimization
• Preferred:
  Open-source contributions in reliability, observability, or infrastructure automation
• Knowledge of WCAG 2.2 AA standards, assistive technologies, and digital accessibility best practices is preferred

Demonstrates expertise in Reliability Engineering, AI/ML Engineering, and Platform Engineering, with a focus on architecting and implementing autonomous systems, intelligent automation solutions, and machine learning platforms. Proficient in cloud engineering and observability practices, ensuring mission-critical system reliability and performance optimization.

• Reliability Engineering
• AI/ML Engineering
• Cloud Engineering
• MLOps
• Multi-Agent Frameworks

• Machine Learning
• Tensor Flow
• Py Torch
• Kubernetes
• Docker
• CI/CD Pipelines
• Infrastructure as Code
• Predictive Analytics
• Anomaly Detection
• Change Management

• Technical Mentorship
• Thought Leadership

• Incident Management
• Problem Management
• Change Management
• Performance Engineering
• Capacity Engineering

• Azure
• GCP
• AWS
• MLflow
• Kubeflow
• Seldon
• Kafka
• Pulsar