Ai Ml Engineer

Role & responsibilities

• Strong programming skills in Python.
• Expertise in machine learning, deep learning, and applied AI techniques.
• Experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras.
• Strong knowledge of data analysis using NumPy, Pandas, and visualization tools.
• Experience in developing models for structured and unstructured data.
• Hands-on experience in computer vision tasks such as image classification, object detection, segmentation, and video analytics.
• Knowledge of advanced computer vision techniques such as YOLO, CNNs, and tracking algorithms.
• Experience in NLP tasks including text classification, sentiment analysis, topic modeling, and conversational AI.
• Exposure to audio and speech analytics, including feature extraction and signal processing.
• Frameworks & Tools:
• Familiarity with Scikit-learn and ML model evaluation techniques.
• Experience with cloud platforms such as AWS or Azure.
• Exposure to tools such as SageMaker, Azure ML, Databricks, or container-based deployment methods.
• Understanding of ML pipeline development, APIs, and microservices-based architectures.
• Additional Knowledge:
• Understanding of deep learning architectures such as CNNs, RNNs, and Transformers.
• Knowledge of NLP techniques and text processing frameworks.
• Understanding of model deployment strategies, including APIs, containers, and cloud platforms.
• Preferred Qualification:
• Experience working on computer vision, NLP, GenAI, or predictive analytics projects.
• Participation in Kaggle competitions, AI hackathons, or open-source AI initiatives.
• Contributions to GitHub repositories, technical blogs, or published AI/ML projects are an added advantage.
• Soft Skills:
• Strong analytical and problem-solving abilities.
• Structured thinking and cognitive skills.
• Good presentation and communication skills.
• High energy, curiosity, and willingness to learn.
• Ability to work independently and collaborate effectively in a team environment.

+ You would be responsible for

• Design, develop, and deploy machine learning and deep learning models for enterprise use cases.
• Lead the development of AI/ML solutions across the full lifecycle, including data preparation, model development, evaluation, deployment, and monitoring.
• Develop algorithms and models for structured and unstructured data analysis.
• Build and optimize models using frameworks such as TensorFlow, PyTorch, and Keras.
• Implement solutions for computer vision tasks including image classification, object detection, segmentation, and video analytics.
• Apply advanced computer vision techniques such as YOLO, CNNs, and tracking algorithms in real-world applications.
• Design and implement NLP-based solutions such as text classification, sentiment analysis, topic modeling, and conversational AI components.
• Work with audio and speech analytics, including feature extraction and signal processing when applicable.
• Conduct Exploratory Data Analysis (EDA) and advanced data analysis using Python libraries such as NumPy, Pandas, and visualization tools.
• Perform data preprocessing, feature engineering, and dataset optimization for ML pipelines.
• Develop scalable ML pipelines and workflows using cloud-based platforms such as AWS or Azure.
• Deploy and operationalize models using tools such as SageMaker, Azure ML, Databricks, or container-based deployment methods.
• Integrate ML models with enterprise systems through APIs, microservices, or batch processing pipelines.
• Monitor model performance, perform tuning, and implement continuous improvement and optimization strategies.
• Maintain proper documentation, experiment tracking, and model versioning using industry best practices.
• Mentor junior engineers and support knowledge sharing within the AI/ML team.
• Stay updated with emerging technologies in AI/ML, Generative AI, Agentic AI, and advanced analytics.