Responsibilities :

- Design, Develop, and Deploy AI/ML Models : Address complex business challenges through AI and ML solutions, ensuring production-grade deployments.

- Build Scalable AI/ML Pipelines : Work on data preprocessing, feature engineering, model training, validation, deployment, and ongoing monitoring of AI/ML models.

- Collaboration with Cross-functional Teams : Collaborate with Data Engineers, Data Scientists, and other stakeholders to implement end-to-end AI/ML solutions.

- Optimize AI/ML Models in Production : Ensure that deployed models are reliable, scalable, and continuously optimized for high performance in production environments.

- Develop APIs and Microservices : Build APIs or microservices to integrate AI/ML models into business solutions, making them usable across different platforms.

- Implement MLOps Practices : Optimize workflows using CI/CD pipelines, automated retraining of models, version control, and monitoring.

- Apply State-of-the-art AI/ML Techniques : Utilize deep learning, NLP, computer vision, and time-series analysis to develop innovative solutions.

- Ensure Ethical AI Practices : Adhere to data privacy, security, and ethical AI standards throughout the entire lifecycle of AI/ML models.

- Stay Up-to-date with Industry Trends : Continuously explore and implement the latest AI/ML techniques, tools, and best practices.

- Assist in Documentation and Best Practices : Help create and maintain standards, workflows, and documentation for data technology and AI/ML projects.

Requirements :

- Strong Expertise in AI/ML Frameworks : Proficiency with TensorFlow, PyTorch, or Scikit-learn for building and deploying models.

- Proficiency in Programming Languages : Strong command of Python or R, including libraries/tools for data science and machine learning.

- Experience with Cloud-based AI/ML Services : Hands-on experience with Microsoft Azure and Databricks.

- MLOps Experience : Expertise in implementing automated pipelines, model versioning, monitoring, and lifecycle management.

- Domain-Specific AI Expertise : Deep knowledge in NLP, computer vision, predictive analytics, or other specialized AI techniques.

- Excellent Problem-Solving Skills : Ability to identify and address technical challenges with innovative and efficient solutions.

- Effective Communication Skills : Strong ability to convey complex technical concepts to both technical and non-technical audiences.

- Attention to Detail : Ability to handle and execute highly specialized projects with a focus on quality.

Desired Skills and Qualifications :

- Familiarity with Distributed Computing : Knowledge of Apache Spark and scalable training techniques for large datasets.

- Experience with GenAI : Hands-on experience in developing enterprise-level Gen AI applications and models for business use cases.

- Knowledge of Data Privacy and Security Standards : Understanding of ethical AI principles and ensuring compliance with relevant privacy and security standards.

- Interpersonal Skills : Excellent collaboration and relationship-building skills with colleagues, stakeholders, and external partners