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. Were 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:
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.). Required Qualifications:
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. Preferred Qualifications:
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.
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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.). Required Qualifications:
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. Preferred Qualifications:
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.
#J-18808-Ljbffr