Job Title: Machine Learning Engineer
Job Summary:
We are looking for a Mid-Level Machine Learning Engineer to develop, optimize, and deploy scalable machine learning models for real-world applications. The ideal candidate should have strong experience in data preprocessing, model development, deep learning architectures, cloud-based deployments, and MLOps practices. This role involves collaborating with data scientists, software engineers, and product teams to build high-performance AI-driven solutions.
Key Responsibilities:
- Design, implement, and optimize machine learning models for classification, regression, clustering, NLP, and computer vision applications
- Develop deep learning architectures (CNNs, RNNs, Transformers, LSTMs) using TensorFlow, PyTorch, and Keras
- Build end-to-end ML pipelines, from data ingestion to model training, evaluation, and deployment
- Perform feature engineering, dimensionality reduction, and hyperparameter tuning for better model performance
- Deploy machine learning models using Docker, Kubernetes, Flask, FastAPI, or cloud platforms (AWS, GCP, Azure)
- Implement MLOps practices, including CI/CD for ML, model versioning, monitoring, and retraining
- Optimize model performance and scalability, ensuring low latency and high accuracy
- Work with big data frameworks (Spark, Dask, Hadoop) for large-scale ML processing
- Implement automated model retraining workflows using Airflow, MLflow, or Kubeflow
- Stay updated on state-of-the-art AI/ML research, implementing cutting-edge algorithms in production
Skills and Knowledge Required:
- Strong programming skills in Python (experience with R or Java is a plus)
- Deep expertise in machine learning algorithms, including deep learning, reinforcement learning, and probabilistic modeling
- Proficiency with ML frameworks (TensorFlow, PyTorch, Keras, Scikit-learn)
- Experience with cloud-based ML services (AWS SageMaker, Google Cloud AI, Azure ML)
- Familiarity with distributed computing (Spark ML, Dask, Ray) for handling large datasets
- Strong understanding of MLOps, including model monitoring, automation, and deployment best practices
- Proficiency in model evaluation, explainability (SHAP, LIME), and A/B testing
- Experience with databases (SQL, NoSQL) and data engineering pipelines
- Ability to work with APIs for integrating ML models into production applications
- Knowledge of NLP, Computer Vision, or Time Series Forecasting (optional but preferred)
Educational Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field
- Certifications in Machine Learning, Deep Learning, Cloud AI (AWS/GCP/Azure), or MLOps are a plus
Experience:
- 3-5 years of experience in machine learning model development, deployment, and optimization
- Experience in handling real-world ML applications, data engineering workflows, and cloud deployments
Key Focus Areas:
- End-to-End ML Model Development & Optimization
- Deep Learning & AI Algorithm Implementation
- Scalable Model Deployment (Cloud, Edge, and API-based Solutions)
- MLOps & CI/CD Pipelines for AI Applications
Tools and Technologies:
- Programming Languages: Python (R, Java optional)
- ML Frameworks: Scikit-learn, TensorFlow, PyTorch, Keras
- Big Data & Distributed ML: Spark ML, Dask, Ray, Hadoop
- Cloud Platforms: AWS SageMaker, Google Cloud AI, Azure ML
- Deployment & MLOps: Docker, Kubernetes, Airflow, MLflow, FastAPI, Flask
- Data Engineering & Storage: SQL, NoSQL, Snowflake, BigQuery, Data Lakes
- Version Control & CI/CD: Git, DVC, Jenkins, Terraform
Other Requirements:
- Ability to work independently and drive ML solutions in a production environment
- Strong analytical and problem-solving skills to tackle complex AI challenges
- Excellent communication and teamwork skills to collaborate with engineers, data scientists, and business stakeholders
- Passion for AI research, innovation, and continuous learning