Job Description:
Machine Learning Engineer
We are seeking a highly skilled Machine Learning Engineer to design, develop, and deploy scalable ML models and AI-driven solutions. The ideal candidate will work closely with data scientists, software engineers, and business stakeholders to build intelligent systems that enhance decision-making, automation, and customer experiences.
Preferred Qualifications:
- Experience with NLP (Transformers, BERT, GPT models) or Computer Vision (CNNs, OpenCV, YOLO, Fast R-CNN).
- Knowledge of Generative AI models and LLMs (Large Language Models).
- Experience with A/B testing, feature selection, and model interpretability techniques.
Why Join Us?
- Work on cutting-edge AI/ML projects with a dynamic and innovative team.
- Opportunity to develop AI solutions impacting real-world business challenges.
- Competitive salary, career growth, and learning opportunities in the evolving AI landscape.
Responsibilities:
Model Development & Deployment
- Design, develop, and train machine learning models for predictive analytics, classification, recommendation systems, NLP, computer vision, and other AI-driven applications.
- Optimize and fine-tune models for accuracy, performance, and efficiency.
- Implement and deploy models using cloud platforms (AWS, Azure, GCP) and MLOps pipelines.
Data Engineering & Feature Engineering
- Preprocess, clean, and structure large datasets for machine learning applications.
- Develop automated feature engineering pipelines for real-time and batch processing.
- Work with structured and unstructured data from multiple sources, ensuring data quality and consistency.
Scalability & Performance Optimization
- Develop scalable ML solutions that integrate seamlessly with production systems.
- Optimize models for speed, memory usage, and computational efficiency.
- Implement distributed training and model parallelization techniques when required.
MLOps & Model Monitoring
- Deploy and manage ML models in production using CI/CD pipelines, Docker, and Kubernetes.
- Monitor model performance, identify drift, and retrain models to maintain accuracy.
- Implement logging, alerting, and monitoring for deployed models using tools like MLflow, TensorBoard, or Prometheus.
Collaboration & Stakeholder Engagement
- Work closely with data scientists, software engineers, and product teams to translate business problems into ML solutions.
- Communicate complex ML concepts and results to non-technical stakeholders effectively.
- Contribute to research and innovation by exploring new algorithms and techniques.
Preferred Qualifications:
- Educational Background: Bachelor's or Master's in Computer Science, Data Science, Artificial Intelligence, or a related field.
- Programming & Frameworks: Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and ML libraries.
- Cloud & MLOps: Experience with AWS (SageMaker, Lambda, S3), Azure (Machine Learning, Blob Storage), or GCP (Vertex AI).
- Data Engineering: Hands-on experience with SQL, Spark, Kafka, and NoSQL databases.
- Algorithm Expertise: Strong understanding of supervised, unsupervised, deep learning, and reinforcement learning algorithms.
- Deployment & Scalability: Experience with Docker, Kubernetes, CI/CD pipelines, and API integration.
- Soft Skills: Strong problem-solving abilities, teamwork, and communication skills.
