Machine Learning Engineer

As a Machine Learning Engineer at our forward-thinking technology company, you will be a key player in our AI initiatives. Your role involves designing, developing, and deploying robust and scalable ML solutions to address real-world business challenges. **Key Responsibilities:** - **Data Handling & Preparation:** - Perform data extraction, integration, preprocessing, and cleansing from various structured and unstructured sources. - Conduct exploratory data analysis (EDA) to identify trends, anomalies, and insights. - Apply vectorization techniques (e.g., TF-IDF, embeddings) where applicable for text and categorical data. - **Feature Engineering & Modeling:** - Apply feature scaling, selection, and engineering techniques to enhance model performance. - Build, train, and evaluate machine learning models for both regression and classification problems. - Tune parameters and hyperparameters including learning rate, regularization factors, and model-specific controls. - **Model Evaluation & Optimization:** - Use appropriate evaluation metrics (e.g., RMSE, F1-score, AUC) to validate model performance. - Optimize model pipelines using techniques like cross-validation, grid/random search, and early stopping. - **Deployment & Monitoring:** - Package and deploy models using APIs, containers (Docker), or scalable services (AWS/GCP/Azure). - Build ML pipelines for continuous training, testing, deployment, and performance monitoring. - Monitor live models and retrain/update based on data drift or accuracy decay. **Qualifications Required:** - **Required Qualifications:** - Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related field. - Strong programming skills in Python, with hands-on experience in NumPy, Pandas, and Scikit-learn. - Deep understanding of machine learning fundamentals: supervised learning, model evaluation, vectorization, parameters vs hyperparameters, and learning rate tuning. - Experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost. - Proficiency in working with large datasets, data wrangling, and pipeline automation. - Familiarity with version control (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 (e.g., Spark, Kafka, Airflow). - Exposure to MLOps and model lifecycle management. - Prior experience in use cases involving computer vision, recommendation systems, or NLP. **Additional Details:** Our company values professionals who understand deep learning concepts such as Transformers, CNNs, RNNs. Experience with small language models (SLMs) and techniques like fine-tuning is a plus. Familiarity with Retrieval-Augmented Generation (RAG) and vector databases like FAISS, Pinecone, or Weaviate is advantageous. Exposure to large language models (LLMs) and enterprise-level integrations, as well as understanding agentic frameworks (e.g., LangChain agents, ReAct) for intelligent workflow orchestration, will be beneficial.,