What are the responsibilities and job description for the Senior Machine Learning Engineer position at Ascentt?
Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We’re hiring passionate builders to shape the future of industrial intelligence.
About The Role
We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning techniques, large-scale data processing, and model deployment in cloud environments. The ideal candidate will be a self-starter with strong problem-solving skills and hands-on experience in building and deploying ML models using big data technologies like PySpark and cloud platforms like Amazon SageMaker.
Key Responsibilities
About The Role
We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning techniques, large-scale data processing, and model deployment in cloud environments. The ideal candidate will be a self-starter with strong problem-solving skills and hands-on experience in building and deploying ML models using big data technologies like PySpark and cloud platforms like Amazon SageMaker.
Key Responsibilities
- Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data.
- Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines.
- Apply a wide range of statistical, machine learning, and deep learning techniques, including but not limited to regression, classification, clustering, time-series forecasting, and NLP.
- Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment.
- Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment.
- Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows.
- Monitor and improve model performance, scalability, and reliability in production.
- Contribute to setting up and maintaining the ML environment and tooling (including environment configuration, CI/CD pipelines for ML, model versioning, etc.).
- 7 years of experience in machine learning, data science, or related fields.
- Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
- Hands-on experience with PySpark for big data processing and model development.
- Proficient in building models on large-scale datasets (terabytes to petabytes).
- Solid understanding of statistical analysis, probability, hypothesis testing, and experimental design.
- Experience with Amazon SageMaker (or similar cloud-based ML platforms).
- Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies.
- Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus.
- Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders.
- Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
- Experience with workflow orchestration tools (e.g., Airflow, Kubeflow).
- Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus.