Building Scalable AI/ML Operations

7 hours ago

Hyderabad City Taluka, Pakistan beBeeDataScientist Full time

Senior AI/ML Operations Engineer

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GSPANN is hiring a Senior AI/ML Operations Engineer. The role focuses on building AIOps/MLOps systems and automating ML pipelines.

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Key Responsibilities

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  • AIOps/MLOps Framework Architect: Design and implement scalable Artificial Intelligence for IT Operations (AIOps) and Machine Learning Operations (MLOps) frameworks.
  • Junior Engineer Mentorship: Share best practices in model deployment and operational excellence with junior engineers and data scientists.
  • Business Alignment: Collaborate with product managers, Site Reliability Engineers (SREs), and other key stakeholders to align technical strategies with business objectives.
  • Engineering Standards: Establish and uphold engineering standards, including Service-Level Agreements (SLAs), Service-Level Indicators (SLIs), and Service-Level Objectives (SLOs) for machine learning and AIOps services.
  • Model CI/CD Pipelines: Design and manage Machine Learning (ML) Continuous Integration/Continuous Deployment (CI/CD) pipelines for model training, testing, deployment, and monitoring using tools like Kubeflow, MLflow, and Apache Airflow.
  • Monitoring Systems: Implement robust monitoring systems to track model performance metrics like drift, latency, and accuracy, and automate retraining workflows where necessary.
  • Model Governance: Lead model governance efforts by ensuring reproducibility, traceability, and compliance with frameworks such as FAIR (Findable, Accessible, Interoperable, Reusable), and maintaining audit logs.
  • AI/ML Solutions: Build AI/ML-powered solutions for proactive infrastructure monitoring, predictive alerting, and intelligent incident resolution.
  • Anomaly Detection: Enhance anomaly detection and root cause analysis by integrating and optimizing observability tools such as Prometheus, Grafana, ELK (Elasticsearch, Logstash, Kibana), Dynatrace, Splunk, and Datadog.
  • Response Workflows: Automate response workflows using predefined playbooks, runbooks, and self-healing systems.
  • Distributed Tracing: Apply statistical techniques and machine learning models to analyze logs, metrics, and distributed traces at scale.">
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Requirements and Qualifications

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  • Bachelor's or Master's Degree: In Computer Science, Data Engineering, Artificial Intelligence, Machine Learning, or a related field.
  • Certifications: AWS/GCP DevOps, Kubernetes, or MLOps are desirable.
  • Experience: 6+ years of hands-on experience in DevOps, MLOps, or AIOps, including at least 2 years in a leadership or senior engineering capacity.
  • Programming Skills: Expert-level coding skills in Python and Bash, with working knowledge of Go or Java.
  • Cloud Providers: Experience with major cloud providers like AWS, GCP, and Azure.
  • CI/CD Tools: Working knowledge of CI/CD tools and infrastructure-as-code technologies like Terraform, Ansible, and Helm.
  • ML Lifecycle Management: In-depth knowledge of ML lifecycle management, performance monitoring, and pipeline orchestration.
  • Observability Platforms: Maintain large-scale observability and telemetry platforms.
  • Streaming Data Technologies: Experience with streaming data technologies such as Apache Kafka, Spark, and Flink.
  • Service Mesh Architecture: Manage service mesh architectures like Istio or Linkerd.
  • Data Privacy and Regulatory Standards: Understanding of data privacy and regulatory standards including GDPR and HIPAA.">
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