Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
Generative AI Lead | 6–8 Years Experience
We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.
Work Schedule
This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.
What You'll Do
Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working modelsLead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparationBuild, train, and iterate on ML models; run experiments, compare candidates, and champion the best solutionPackage and deploy models into production-ready services using containerization and MLOps best practicesOwn post-deployment health — set up monitoring, track model performance, and drive continuous improvementYour Technical Toolkit
Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)
Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation
Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting
Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines
Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data
Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)
Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect
Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra
✅ What Sets You Apart
A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it upProven experience shipping models to production in cloud-agnostic, API-first architecturesComfortable collaborating with engineering teams via version control and CI/CD workflowsGenerative AI exposure is a strong plus — and increasingly central to this roleIndustryTechnology, Information and InternetEmployment TypeContract
