Oct 17
Confidential
Mid Level Career (5+ yrs experience)
$125,000 - $150,000
No Traveling
IT - Data Science
Role and Responsibilities
Model Development
• Collaborate with data scientists and SMEs to develop ML models using curated datasets.
• Conduct experiments, prototypes, and proof-of-concepts to validate model performance.
• Create scalable and reusable training pipelines using Databricks notebooks and MLflow.
Implementation and Optimization
• LLMs (Large Language Models), RAGs, and AI agent systems for various business applications.
Deployment & MLOps
• Operationalize models with robust CI/CD workflows.
• Deploy models using MLflow, SageMaker, or custom APIs.
• Monitor production models for accuracy, drift, and latency; manage retraining schedules.
Data Integration & Architecture Alignment
• Work closely with Data Engineering to align ML pipelines with the Bronze, Silver, Gold layers of a Medallion Architecture.
• Engineer high-quality features and maintain training/inference pipelines.
Cloud and Platform Engineering
• Leverage AWS services including S3, EC2, Lambda, SageMaker, and Step Functions.
Collaboration & Documentation
• Document ML artifacts, processes, and performance outcomes.
• Contribute to agile project ceremonies and maintain a feedback loop with stakeholders.
• Share knowledge and mentor junior team members.
Required Skills:
• 5+ years of experience in ML Engineering or Applied Machine Learning.
• Strong Python skills and hands-on experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
• Proficient with Databricks, MLflow, and PySpark.
• Solid understanding of model lifecycle and MLOps practices.
• Experience with AWS-based data infrastructure and related DevOps practices.
• Demonstrated ability to productionize models and integrate with business system
• Strong understanding of mathematics and statistics relevant to machine learning and AI.
• Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.).
• Solid background in software engineering principles and best practices.
• Hands-on experience with model training frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
• Experience with MLOps tools and workflows, particularly on AWS (SageMaker, Lambda, S3, etc.).
• Practical experience with LLMs, RAGs, and AI agent architectures.
• Proficiency with the Databricks platform for data engineering and ML pipelines.
• Advanced programming skills in Python.
• Excellent communication and teamwork abilities.
Preferred Skills:
• Experience building and deploying interactive UIs for AI models using Streamlit, Gradio, or similar frameworks for rapid prototyping and real-time model interactions
• Business acumen and ability to align AI solutions with organizational goals.
• Optimize compute and storage resources for performance and cost-efficiency.
Model Development
• Collaborate with data scientists and SMEs to develop ML models using curated datasets.
• Conduct experiments, prototypes, and proof-of-concepts to validate model performance.
• Create scalable and reusable training pipelines using Databricks notebooks and MLflow.
Implementation and Optimization
• LLMs (Large Language Models), RAGs, and AI agent systems for various business applications.
Deployment & MLOps
• Operationalize models with robust CI/CD workflows.
• Deploy models using MLflow, SageMaker, or custom APIs.
• Monitor production models for accuracy, drift, and latency; manage retraining schedules.
Data Integration & Architecture Alignment
• Work closely with Data Engineering to align ML pipelines with the Bronze, Silver, Gold layers of a Medallion Architecture.
• Engineer high-quality features and maintain training/inference pipelines.
Cloud and Platform Engineering
• Leverage AWS services including S3, EC2, Lambda, SageMaker, and Step Functions.
Collaboration & Documentation
• Document ML artifacts, processes, and performance outcomes.
• Contribute to agile project ceremonies and maintain a feedback loop with stakeholders.
• Share knowledge and mentor junior team members.
Required Skills:
• 5+ years of experience in ML Engineering or Applied Machine Learning.
• Strong Python skills and hands-on experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
• Proficient with Databricks, MLflow, and PySpark.
• Solid understanding of model lifecycle and MLOps practices.
• Experience with AWS-based data infrastructure and related DevOps practices.
• Demonstrated ability to productionize models and integrate with business system
• Strong understanding of mathematics and statistics relevant to machine learning and AI.
• Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.).
• Solid background in software engineering principles and best practices.
• Hands-on experience with model training frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
• Experience with MLOps tools and workflows, particularly on AWS (SageMaker, Lambda, S3, etc.).
• Practical experience with LLMs, RAGs, and AI agent architectures.
• Proficiency with the Databricks platform for data engineering and ML pipelines.
• Advanced programming skills in Python.
• Excellent communication and teamwork abilities.
Preferred Skills:
• Experience building and deploying interactive UIs for AI models using Streamlit, Gradio, or similar frameworks for rapid prototyping and real-time model interactions
• Business acumen and ability to align AI solutions with organizational goals.
• Optimize compute and storage resources for performance and cost-efficiency.
group id: 10490845
