Machine Learning Engineer Needed for Predictive Analytics and AI Model Development

We're seeking an experienced Machine Learning Engineer to develop and deploy predictive models and AI solutions that will enhance our data-driven decision-making capabilities and automate complex business processes.

Project Overview:

Build machine learning models for customer behavior prediction, recommendation systems, and automated data classification. The solution should include model training, evaluation, deployment, and ongoing monitoring.

Key Responsibilities:

Analyze and preprocess large datasets for ML model training

Design and implement machine learning algorithms and models

Train, validate, and optimize models for accuracy and performance

Deploy models to production environments (cloud or on-premise)

Create data pipelines for automated model retraining

Implement A/B testing framework for model performance

Develop APIs for model inference and integration

Monitor model performance and handle model drift

Create visualizations and dashboards for insights

Required Skills:

3+ years of machine learning/data science experience

Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn)

Experience with data preprocessing and feature engineering

Knowledge of supervised and unsupervised learning algorithms

Understanding of deep learning architectures (CNN, RNN, Transformers)

Experience with NLP techniques and libraries (NLTK, spaCy, Hugging Face)

Proficiency in SQL and NoSQL databases

Cloud platform experience (AWS SageMaker, Google AI Platform, Azure ML)

Model deployment and MLOps practices

Statistical analysis and data visualization (Pandas, Matplotlib, Seaborn)

Technical Stack:

Python 3.8+

ML Frameworks: TensorFlow 2.x, PyTorch, scikit-learn

Data Processing: Pandas, NumPy, Apache Spark

Model Deployment: Docker, Flask/FastAPI, Kubernetes

Cloud: AWS/GCP/Azure ML services

Version Control: Git, DVC (Data Version Control)

Experiment Tracking: MLflow, Weights & Biases

Use Cases:

Customer churn prediction

Product recommendation system

Sentiment analysis and text classification

Image recognition and object detection

Time series forecasting

Anomaly detection

Deliverables:

Trained and validated ML models with performance metrics

Model training pipeline and documentation

Deployed model with REST API endpoints

Data preprocessing and feature engineering code

Model performance monitoring dashboard

Technical documentation and architecture diagrams

Jupyter notebooks with experiments and analysis

Model retraining automation scripts

Budget: $50 - $100/hour (Hourly) or $8,000 - $15,000 (Fixed project)

Timeline: 6-10 weeks