Job Description:

We are seeking a highly skilled AI/ML Engineer to design, develop, and deploy scalable machine learning and artificial intelligence solutions that solve real-world business problems. The ideal candidate will have strong expertise in data science, machine learning model development, MLOps practices, and cloud-based AI services. This role involves working closely with data engineers, product teams, and software developers to build end-to-end AI/ML systems, optimize performance, and ensure seamless deployment into production environments.

Key Responsibilities:

• Design, build, and optimize machine learning and deep learning models for predictive analytics, classification, recommendation systems, NLP, computer vision, and other use cases.

• Collaborate with data engineers to preprocess, clean, and transform structured and unstructured datasets.

• Implement MLOps pipelines for model training, deployment, monitoring, and version control.

• Optimize ML models for performance, scalability, and cost efficiency in production.

• Work with cloud AI/ML platforms (AWS Sagemaker, Azure ML, GCP Vertex AI) to deploy and manage models.

• Research and integrate the latest AI/ML frameworks, libraries, and algorithms.

• Partner with product and business stakeholders to translate requirements into ML solutions.

• Ensure models adhere to governance, compliance, and ethical AI standards.

• Conduct A/B testing, model evaluation, and performance monitoring to ensure accuracy and reliability.

• Provide technical expertise and mentorship to team members on ML best practices.

Professional Skills:

• Strong programming experience in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).

• Proficiency in SQL and experience with NoSQL databases (MongoDB, Cassandra).

• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes, Git).

• Knowledge of cloud AI services: AWS SageMaker, Azure ML Studio, GCP Vertex AI.

• Expertise in machine learning algorithms (regression, classification, clustering, ensemble methods).

• Experience with deep learning architectures (CNNs, RNNs, Transformers).

• Strong understanding of NLP, computer vision, and generative AI.

• Familiarity with big data frameworks (Spark, Hadoop) for large-scale ML training.