Ai / ML Engineer

We are seeking a highly skilled AI / ML Engineer to join our team. The ideal candidate will have deep expertise in developing, training, and deploying machine learning models, focusing on optimizing performance and scalability for real-world applications. The candidate should have experience in MLOps, model lifecycle management, and backend development using Flask or FastAPI for AI services. Proficiency in containerization technologies like Docker is essential for seamless deployment.

Key Responsibilities :

Design, develop, and fine-tune machine learning models for various AI-driven applications.

Implement scalable and efficient model training and inference pipelines.

Develop and maintain backend services using Flask or FastAPI to support AI workflows.

Build and optimize APIs for model inference, feature extraction, and real-time processing.

Enhance model performance through techniques such as hyperparameter tuning, quantization, and optimization.

Deploy ML models efficiently using Docker and integrate with cloud platforms (AWS, GCP, or Azure).

Implement and maintain MLOps practices, including model versioning, monitoring, and automated retraining.

Collaborate with data scientists, ML engineers, and software developers to integrate AI models into production systems.

Research and experiment with state-of-the-art AI / ML techniques to drive innovation.

Requirements

Strong proficiency in Python and AI / ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn.

Experience in training, evaluating, and deploying machine learning models at scale.

Hands-on experience with Flask or FastAPI for AI-centric backend development.

Expertise in building and deploying RESTful APIs for AI applications.

Proficiency in Docker and container orchestration tools (Kubernetes is a plus).

Experience with cloud-based AI model deployment (AWS SageMaker, GCP Vertex AI, or Azure ML).

Knowledge of MLOps tools and best practices for continuous model training and monitoring.

Strong understanding of data preprocessing, feature engineering, and model interpretability.

Ability to implement optimization techniques for deep learning models.

Strong problem-solving skills and ability to work in a collaborative AI / ML-focused environment.

Preferred Qualifications :

Experience with advanced model deployment strategies, such as edge AI and federated learning.

Familiarity with distributed training frameworks like Horovod or Ray.

Hands-on experience with AI model explainability tools (SHAP, LIME, etc.).

Understanding of reinforcement learning, generative models, or NLP techniques.

Familiarity with MLOps pipelines using tools like MLflow, Kubeflow, or Airflow.

Experience in working with large-scale datasets and real-time AI applications.

Knowledge of security and compliance considerations for AI deployments.