- Company Name
- ALS
- Job Title
- Machine Learning Engineer
- Job Description
-
**Job Title:** Machine Learning Engineer
**Role Summary:** Design, develop, and maintain end‑to‑end machine learning systems for geological data analytics. Responsibilities include building scalable pipelines, deploying real‑time and batch inference services, and collaborating with data scientists, geologists, and backend teams to integrate models into production APIs.
**Expectations:**
- 4+ years of experience in machine learning or applied data engineering.
- Proven track record of deploying models on cloud infrastructure (AWS preferred).
- Strong Python programming and scientific library expertise.
**Key Responsibilities:**
- Build and maintain ML microservices, training pipelines, and inference workflows using AWS Lambda, EC2, ECS, and Fargate.
- Design serving infrastructure for batch and real‑time inference.
- Develop and manage experimentation pipelines with MLflow, Jupyter Notebooks, and Conda.
- Train and tune deep learning models (PyTorch/TensorFlow) for supervised, unsupervised, and computer‑vision tasks.
- Optimize performance on GPU (CUDA, NVIDIA) environments.
- Collaborate with data science, geology, backend, and product teams to integrate models into production APIs.
**Required Skills:**
- Python and machine‑learning libraries: PyTorch, TensorFlow, scikit‑learn, scikit‑image.
- AWS services: Lambda, EC2, ECS, Fargate, SageMaker (preferred).
- MLOps practices: model lifecycle management, experiment tracking (MLflow, Kubeflow, Airflow).
- GPU‑accelerated training: CUDA, NVIDIA GPUs.
- Reproducible development: Jupyter, Conda, version control.
- Strong understanding of supervised/unsupervised learning and deep learning fundamentals.
- Excellent collaboration and communication across cross‑functional teams.
**Required Education & Certifications:**
- Bachelor’s or higher degree in Computer Science, Engineering, Applied Mathematics, Geoscience, or related field.
- Certifications in cloud platforms (e.g., AWS Certified Machine Learning – Specialty) or MLOps (optional but preferred).