Job Title: Machine Learning Engineer
Job Summary:
We are looking for a Senior Machine Learning Engineer to lead the design, development, and deployment of scalable AI/ML solutions for enterprise applications. The ideal candidate should have extensive experience in machine learning algorithms, deep learning architectures, cloud-based AI solutions, and MLOps best practices. This role involves mentoring junior engineers, optimizing ML models, working with large datasets, and integrating AI-driven insights into business applications.
Key Responsibilities:
- Lead the development and deployment of machine learning models for real-world AI applications
- Architect and optimize deep learning models (CNNs, RNNs, Transformers, GANs) using TensorFlow, PyTorch, and Keras
- Develop and manage full ML pipelines, including data preprocessing, feature engineering, training, and deployment
- Implement scalable AI solutions using cloud ML services (AWS SageMaker, Google Cloud AI, Azure ML)
- Improve model performance, interpretability, and generalization using advanced techniques (transfer learning, ensembling, hyperparameter tuning)
- Deploy ML models in production environments using Docker, Kubernetes, and CI/CD pipelines
- Build and maintain MLOps frameworks, ensuring model retraining, monitoring, and versioning for AI-driven systems
- Work with big data processing tools (Spark ML, Dask, Hadoop) to train models on large-scale datasets
- Collaborate with data scientists, engineers, and business teams to align AI solutions with business objectives
- Stay ahead of AI advancements, research trends, and industry best practices, integrating cutting-edge technologies into projects
Skills and Knowledge Required:
- Expert proficiency in Python (R, Scala, or Java is a plus)
- Extensive experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch, Keras)
- Deep knowledge of supervised, unsupervised, and reinforcement learning techniques
- Proficiency in deep learning architectures (CNNs, RNNs, LSTMs, Transformers, GANs, and Autoencoders)
- Strong understanding of big data and distributed computing frameworks (Apache Spark, Dask, Ray)
- Expertise in cloud-based AI solutions (AWS SageMaker, Google Cloud AI, Azure ML, Snowflake ML)
- Hands-on experience in deploying AI models via REST APIs, Kubernetes, and serverless architectures
- Strong background in MLOps, including model monitoring, version control (MLflow, DVC), and CI/CD for ML
- Experience with feature engineering, data augmentation, and transfer learning
- Knowledge of AI ethics, bias detection, explainability (SHAP, LIME), and fairness in ML models
- Experience with graph neural networks (GNNs), reinforcement learning, or multimodal AI (preferred)
Educational Qualifications:
- Bachelor’s, Master’s, or PhD in Computer Science, Data Science, AI, Mathematics, or related field
- Certifications in Machine Learning, Deep Learning, Cloud AI, or MLOps (AWS, Google, Coursera, Udacity, Fast.ai) are a plus
Experience:
- 5-8+ years of hands-on experience in machine learning, AI model development, and production deployment
- Proven experience in leading AI-driven projects, working with large datasets, and optimizing AI performance
Key Focus Areas:
- End-to-End Machine Learning Model Development & Deployment
- Deep Learning & Advanced AI Architectures
- Scalable AI Solutions & Cloud-Based Machine Learning
- MLOps & Model Lifecycle Management
- AI Research & Cutting-Edge Innovation
Tools and Technologies:
- Programming Languages: Python (R, Scala, Java optional)
- ML Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn
- Big Data & Distributed ML: Apache Spark, Dask, Ray, Hadoop
- Cloud Platforms: AWS SageMaker, Google Cloud AI, Azure ML, Snowflake ML
- Deployment & MLOps: Docker, Kubernetes, Airflow, MLflow, FastAPI, Flask
- Data Engineering & Storage: SQL, NoSQL, Snowflake, BigQuery, Delta Lake
- Version Control & CI/CD: Git, DVC, Jenkins, Terraform
Other Requirements:
- Strong leadership skills and the ability to mentor junior AI engineers
- Excellent problem-solving and critical-thinking abilities
- Ability to drive AI adoption across teams and departments
- Passion for research, innovation, and applying AI to solve complex business challenges