Machine Learning Engineer

As a forward-thinking technology company that is dedicated to harnessing artificial intelligence and machine learning to create impactful solutions, we are currently seeking a talented Machine Learning Engineer to join our team. In this role, you will be tasked with the design, development, and deployment of robust and scalable machine learning solutions to address real-world business challenges. Your responsibilities will include: Data Handling & Preparation: - Conducting data extraction, integration, preprocessing, and cleansing from various structured and unstructured sources. - Performing exploratory data analysis (EDA) to identify trends, anomalies, and insights. - Applying vectorization techniques such as TF-IDF and embeddings for text and categorical data. Feature Engineering & Modeling: - Implementing feature scaling, selection, and engineering techniques to optimize model performance. - Developing, training, and evaluating machine learning models for regression and classification tasks. - Tuning parameters and hyperparameters, including learning rate, regularization factors, and model-specific controls. Model Evaluation & Optimization: - Utilizing appropriate evaluation metrics like RMSE, F1-score, and AUC to validate model performance. - Optimizing model pipelines through techniques like cross-validation, grid/random search, and early stopping. Deployment & Monitoring: - Packaging and deploying models using APIs, containers (Docker), or scalable services (AWS/GCP/Azure). - Establishing ML pipelines for continuous training, testing, deployment, and performance monitoring. - Monitoring live models and updating them based on data drift or accuracy decay. Qualifications/Experience/Competencies: Required Qualifications: - Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. - Proficiency in Python programming, with practical experience in NumPy, Pandas, and Scikit-learn. - Strong understanding of machine learning fundamentals, including supervised learning, model evaluation, vectorization, parameters vs hyperparameters, and learning rate tuning. - Experience with ML frameworks like TensorFlow, PyTorch, or XGBoost. - Ability to work with large datasets, perform data wrangling, and automate pipelines. - Familiarity with version control using Git and CI/CD tools. Preferred Qualifications: - Experience in deploying ML models using Flask/FastAPI, Docker, or Kubernetes. - Familiarity with SQL and big data tools such as Spark, Kafka, and Airflow. - Exposure to MLOps and model lifecycle management. - Previous involvement in use cases related to computer vision, recommendation systems, or NLP. Nice to Have: - Knowledge of deep learning techniques like Transformers, CNNs, and RNNs. - Experience with small language models (SLMs) and fine-tuning techniques. - Familiarity with Retrieval-Augmented Generation (RAG) and vector databases like FAISS, Pinecone, or Weaviate. - Exposure to large language models (LLMs) and enterprise-level integrations. - Understanding of agentic frameworks such as LangChain agents and ReAct for intelligent workflow orchestration.,