We are seeking a Senior AI Engineer with more than six years of experience designing, developing, and maintaining AI-based systems. The ideal candidate will have advanced expertise in Python, R, and Java, with deep knowledge of machine learning algorithms, neural networks, data modeling, and secure AI practices. This role involves architecting intelligent solutions, guiding AI strategy, and mentoring junior engineers, while ensuring scalability, security, and compliance in enterprise AI systems.

Key Responsibilities

Lead the design, development, and deployment of AI and machine learning systems.

Architect secure AI models using encryption methods and compliance standards.

Build and optimize neural network–based solutions to address business needs.

Translate business requirements into scalable AI/ML models and intelligent applications.

Oversee data modeling, engineering, and preprocessing pipelines.

Integrate cloud-based AI and machine learning services (AWS SageMaker, Azure ML, GCP AI).

Drive MLOps practices for continuous deployment, monitoring, and retraining of models.

Communicate complex AI concepts effectively to both technical and business stakeholders.

Mentor junior AI engineers and contribute to AI capability building across teams.

Ensure compliance with secure AI development practices and emerging AI governance frameworks.

Mandatory Skills

Advanced expertise in Python, R, and Java for AI/ML development.

Strong knowledge of machine learning models, algorithms, and encryption methods.

Deep understanding of neural networks and their applications in AI solutions.

Expertise in data modeling, engineering, and preprocessing.

Proficiency in secure AI practices, including compliance and governance.

Familiarity with cloud-based AI/ML services (AWS, Azure, GCP).

Strong ability to communicate AI concepts to technical and non-technical audiences.

Desirable Skills

Experience designing AI/ML architecture at the enterprise level.

Hands-on experience with MLOps pipelines (CI/CD for ML, monitoring, retraining).

Knowledge of deep learning frameworks (TensorFlow, PyTorch, Keras).

Familiarity with ethical AI practices and emerging regulatory standards.

Leadership experience mentoring AI teams or leading research initiatives.

Exposure to big data ecosystems (Hadoop, Spark, Kafka) for large-scale AI training.