З/п не указана
Создаёт и поддерживает инфраструктуру и инструменты для разработки, развертывания и мониторинга ML моделей.
Инженер MLOps среднего/старшего уровня
О роли
Infrastructure & Platform Development
- Design and implement scalable ML infrastructure on premises and cloud platforms
- Build and maintain ML experimentation and production environments
- Develop and manage container orchestration systems for ML workloads
- Implement GPU resource management and optimization strategies
- Design storage solutions for datasets, models, and artifacts
ML Pipeline & Automation
- Create CI/CD pipelines for ML model training, validation, and deployment
- Implement automated model retraining and versioning systems
- Build orchestration workflows for data processing and model training
- Develop automated testing frameworks for ML models and pipelines
- Design and implement feature stores for feature engineering and reuse
Monitoring & Operations
- Implement model monitoring systems for performance, drift, and data quality
- Set up logging, alerting, and observability for ML systems
- Establish model governance and compliance tracking
- Create dashboards for model performance and infrastructure metrics
- Develop incident response procedures for production ML systems
Collaboration & Best Practices
- Partner with data scientists and AI engineers to productionize ML models
- Establish MLOps best practices and standards across teams
- Provide technical guidance on deployment architectures
- Document processes, systems, and runbooks
- Mentor junior engineers and data scientists on MLOps practices
Xüsusi tələblər
Education
- Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
- Master's degree preferred but not required with sufficient practical experience
Experience
- 2+ years working as ML/Software/DevOps Engineer
- Proven track record of building production ML systems at scale
- Experience supporting data science teams in enterprise environments
Technical Skills
- Strong proficiency in Python and some experience with at least one low-level programming language (C/C++, Go, Rust, or similar)
- Deep understanding of containerization (Docker, Kubernetes)
- Hands-on experience with CI/CD tools (Jenkins/GitLab CI/GitHub Actions, or similar)
- Knowledge of ML frameworks (TensorFlow/PyTorch/scikit-learn, or similar)
- Experience with workflow orchestration (Airflow/Kubeflow/Prefect, or similar)
- Hands-on experience with experiment tracking tools (MLflow/ClearML, or similar)
- Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack, or similar) for infrastructure and ML system monitoring
Core Competencies
- Solid understanding of ML lifecycle and model development processes
- Strong Linux/Unix systems administration skills
- Experience with version control systems (Git) and branching strategies
- Knowledge of networking, security, and compliance in cloud and on-prem environments
- Understanding of distributed computing and parallel processing
- Knowledge of microservices architecture and API design
Technical skills:
- Experience with cloud platforms (Azure ML, AWS SageMaker, or GCP Vertex AI)
- Experience with GitOps practices and tools (ArgoCD, Flux, GitLab with GitOps, or similar) for declarative infrastructure and ML pipeline management
- Hands-on experience with hyperparameter optimization tools (Optuna/Ray Tune/Hyperopt/Katib, or similar)
- Experience with distributed training frameworks
- Experience with model serving frameworks (TensorFlow Serving/TorchServe/Triton/ MLServer, or similar
- Experience with GPU optimization (CUDA/TensorRT/ONNX Runtime, or similar)
- Knowledge of GPU allocation, sharing, management and profiling
LLM Ops:
- Experience with LLM inference frameworks (vLLM/TGI/TensorRT-LLM, or similar)
- Familiarity with agent orchestration frameworks (LangChain/LangGraph/LlamaIndex, or similar)
- Experience with LLM optimization: quantization, KV cache management, continuous batching
- Experience with prompt engineering and versioning tools (LangSmith/PromptLayer/Weights & Biases Prompts/Helicone, or similar)
Soft Skills:
- Strong problem-solving and debugging abilities
- Excellent communication skills with both technical and non-technical stakeholders
- Ability to work independently and manage multiple priorities
- Collaborative mindset with emphasis on enabling others
- Adaptability to rapidly changing technology landscape
- Pragmatic approach to balancing innovation with reliability
Infrastructure & Platform Development
- Design and implement scalable ML infrastructure on premises and cloud platforms
- Build and maintain ML experimentation and production environments
- Develop and manage container orchestration systems for ML workloads
- Implement GPU resource management and optimization strategies
- Design storage solutions for datasets, models, and artifacts
ML Pipeline & Automation
- Create CI/CD pipelines for ML model training, validation, and deployment
- Implement automated model retraining and versioning systems
- Build orchestration workflows for data processing and model training
- Develop automated testing frameworks for ML models and pipelines
- Design and implement feature stores for feature engineering and reuse
Monitoring & Operations
- Implement model monitoring systems for performance, drift, and data quality
- Set up logging, alerting, and observability for ML systems
- Establish model governance and compliance tracking
- Create dashboards for model performance and infrastructure metrics
- Develop incident response procedures for production ML systems
Collaboration & Best Practices
- Partner with data scientists and AI engineers to productionize ML models
- Establish MLOps best practices and standards across teams
- Provide technical guidance on deployment architectures
- Document processes, systems, and runbooks
- Mentor junior engineers and data scientists on MLOps practices
Xüsusi tələblər
Education
- Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
- Master's degree preferred but not required with sufficient practical experience
Experience
- 2+ years working as ML/Software/DevOps Engineer
- Proven track record of building production ML systems at scale
- Experience supporting data science teams in enterprise environments
Technical Skills
- Strong proficiency in Python and some experience with at least one low-level programming language (C/C++, Go, Rust, or similar)
- Deep understanding of containerization (Docker, Kubernetes)
- Hands-on experience with CI/CD tools (Jenkins/GitLab CI/GitHub Actions, or similar)
- Knowledge of ML frameworks (TensorFlow/PyTorch/scikit-learn, or similar)
- Experience with workflow orchestration (Airflow/Kubeflow/Prefect, or similar)
- Hands-on experience with experiment tracking tools (MLflow/ClearML, or similar)
- Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack, or similar) for infrastructure and ML system monitoring
Core Competencies
- Solid understanding of ML lifecycle and model development processes
- Strong Linux/Unix systems administration skills
- Experience with version control systems (Git) and branching strategies
- Knowledge of networking, security, and compliance in cloud and on-prem environments
- Understanding of distributed computing and parallel processing
- Knowledge of microservices architecture and API design
Technical skills:
- Experience with cloud platforms (Azure ML, AWS SageMaker, or GCP Vertex AI)
- Experience with GitOps practices and tools (ArgoCD, Flux, GitLab with GitOps, or similar) for declarative infrastructure and ML pipeline management
- Hands-on experience with hyperparameter optimization tools (Optuna/Ray Tune/Hyperopt/Katib, or similar)
- Experience with distributed training frameworks
- Experience with model serving frameworks (TensorFlow Serving/TorchServe/Triton/ MLServer, or similar
- Experience with GPU optimization (CUDA/TensorRT/ONNX Runtime, or similar)
- Knowledge of GPU allocation, sharing, management and profiling
LLM Ops:
- Experience with LLM inference frameworks (vLLM/TGI/TensorRT-LLM, or similar)
- Familiarity with agent orchestration frameworks (LangChain/LangGraph/LlamaIndex, or similar)
- Experience with LLM optimization: quantization, KV cache management, continuous batching
- Experience with prompt engineering and versioning tools (LangSmith/PromptLayer/Weights & Biases Prompts/Helicone, or similar)
Soft Skills:
- Strong problem-solving and debugging abilities
- Excellent communication skills with both technical and non-technical stakeholders
- Ability to work independently and manage multiple priorities
- Collaborative mindset with emphasis on enabling others
- Adaptability to rapidly changing technology landscape
- Pragmatic approach to balancing innovation with reliability
Локация
Bakı
Опыт
Senior
Занятость
Полная занятость
Зарплата
Не указана
Опубликовано
7 августа 2026
О компании
Caspian Innovation Center
İnformasiya və kommunikasiya, Proqram təminatı və İT xidmətləri · Baku
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