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

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)
• Deep understanding of containerization (Docker, Kubernetes)
• Hands-on experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions, etc.)
• Knowledge of ML frameworks (TensorFlow, PyTorch, scikit-learn)
• Experience with workflow orchestration (Airflow, Kubeflow, Prefect, etc.)
• Hands-on experience with experiment tracking tools (MLflow, ClearML)

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

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

Preferred Qualifications:

If you know at least 3+ skills from the sections below, please apply.

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) for declarative infrastructure and ML pipeline management
• Experience with feature stores (Feast, Tecton, Hopsworks, or similar)
• Experience with model monitoring solutions (Evidently, WhyLabs, Fiddler, Arize, Whylogs)
• Experience with ML explainability tools (SHAP, LIME, Captum, Alibi, InterpretML)
• Hands-on experience with hyperparameter optimization tools (Optuna, Ray Tune, Hyperopt, Katib)
• Experience with distributed training frameworks (Ray Train, Horovod, DeepSpeed, PyTorch DDP, Megatron)
• Experience with model serving frameworks (TensorFlow Serving, TorchServe, Triton, MLServer, or similar)
• Experience with data versioning tools (DVC, Pachyderm, LakeFS)
• Experience with GPU optimization (CUDA, TensorRT, ONNX Runtime, flash-attention)
• Knowledge of GPU allocation, sharing, management and profiling

LLM Ops:

• Experience with LLM inference frameworks (vLLM, TGI, TensorRT-LLM)
• Familiarity with agent orchestration frameworks (LangChain, LangGraph, LlamaIndex)
• Experience with LLM optimization: quantization, KV cache management, continuous batching
• Experience with prompt engineering and versioning tools (LangSmith, PromptLayer, Weights & Biases Prompts, Helicone)We offer
• 5/2, 09.00-18.00;
• Meal allowance;
• Annual performance bonuses;
• Corporate health program: VIP voluntary insurance and special discounts for gyms;
• Access to Digital Learning Platforms.