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.