As an AI/ML Engineer with expertise in Python, you will play a crucial role in developing and deploying cutting-edge machine learning models and AI-driven applications. You will work with large datasets, apply advanced algorithms, and design innovative solutions that enhance business processes, improve decision-making, and provide actionable insights.

Key Responsibilities:
Develop Machine Learning Models: Design, build, and deploy machine learning models using Python and popular libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras.
Data Preprocessing & Feature Engineering: Clean, process, and prepare datasets for model training. Apply feature engineering techniques to ensure the models are built on clean, relevant data.
Algorithm Development: Apply machine learning algorithms (supervised and unsupervised) and deep learning techniques to solve complex problems like classification, regression, clustering, and time-series forecasting.
Model Evaluation & Optimization: Assess model performance using appropriate metrics (e.g., accuracy, precision, recall, AUC) and fine-tune hyperparameters to optimize accuracy and efficiency.
AI Integration: Integrate machine learning models into existing systems or products, ensuring seamless performance and scalability.
Automation & Process Improvement: Automate repetitive tasks and improve workflows through intelligent algorithms and AI solutions.
Collaboration: Work closely with cross-functional teams (data scientists, engineers, product managers) to align AI/ML solutions with business objectives.
Research & Innovation: Stay updated with the latest trends and advancements in AI and ML and apply innovative techniques to continually improve models and systems.

Required Skills:
Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
Strong understanding of machine learning algorithms, deep learning models, and AI techniques.
Experience in data preprocessing, feature engineering, and model evaluation.
Knowledge of cloud computing platforms (e.g., AWS, Google Cloud, Azure) and deployment of ML models in production environments.
Familiarity with data visualization tools and libraries (e.g., Matplotlib, Seaborn).
Excellent problem-solving and analytical skills.
Ability to work both independently and in collaborative, cross-functional teams.