Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer to join our team and drive the development of advanced AI-driven solutions. You will be responsible for designing, deploying, and optimizing machine learning models that power real-world applications. This role requires deep expertise in model architecture, data engineering, MLOps, and production-scale AI systems.

What you'll do:

• Design, develop, and deploy scalable, production-ready machine learning models for real-world applications.
• Architect and implement end-to-end ML pipelines, from data preprocessing to model monitoring and retraining.
• Optimize model performance using techniques like hyperparameter tuning, feature engineering, and distributed training.
• Implement deep learning architectures using TensorFlow, PyTorch, or JAX for tasks such as computer vision, NLP, or recommendation systems.
• Leverage large-scale datasets from structured and unstructured sources, applying advanced techniques such as self-supervised learning, transformers, and graph neural networks.
• Develop real-time and batch inference systems, integrating with cloud platforms (AWS SageMaker, Google Cloud Platform Vertex AI, Azure ML).
• Utilize MLOps best practices, automating model versioning, monitoring, and retraining using Kubernetes, MLflow, or TFX.
• Work closely with data engineers, software developers, and product managers to align machine learning solutions with business objectives.
• Optimize ML models for latency, throughput, and cost-efficiency in cloud and edge environments.
• Stay ahead of emerging ML trends, contributing to research initiatives and open-source projects.

What we're looking for:

• 5+ years of experience in Machine Learning, Deep Learning, or AI-focused software engineering.
• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and familiarity with C++ or Rust for performance optimization.
• Expertise in ML model deployment using Docker, Kubernetes, and cloud services like AWS SageMaker, Google Cloud Platform Vertex AI, or Azure ML.
• Deep understanding of data pipelines and distributed computing using tools like Apache Spark, Ray, or Dask.
• Experience with feature stores (Feast, Tecton), vector databases (FAISS, Pinecone), and real-time model serving.
• Knowledge of ML lifecycle management, versioning (DVC, MLflow), and monitoring (Prometheus, Grafana).
• Strong grasp of probabilistic modeling, reinforcement learning, and generative AI techniques.
• Familiarity with LLMs, fine-tuning transformer models (Hugging Face, OpenAI API), and multimodal learning is a plus.
• Ability to translate complex ML concepts into scalable solutions that drive business impact.
• Experience working in high-performance computing (HPC) environments for large-scale ML workloads.

Nice to Have

• Experience in edge AI model deployment (TensorFlow Lite, ONNX, Nvidia TensorRT).
• Knowledge of differential privacy, federated learning, and adversarial machine learning.
• Contributions to AI research papers, open-source projects, or patents in ML/AI.

To be considered for this opportunitty all you need to do is sign up, make sure to complete your profile to be reviewed by our Matching team! If you're the right fit, we'll reach out to you. Due to the high volume of applicants, we may not always provide feedback, but your profile will be considered for this and future openings.