Job Summary

This role is responsible for leading the design and implementation of advanced AI and machine learning architectures that drive strategic business outcomes. The individual will provide architectural leadership, ensuring solutions leverage state-of-the-art technologies and best practices. They will guide cross-domain teams, establish governance standards, and align technical direction with enterprise objectives, fostering innovation and excellence in AI/ML delivery.

Key Responsibilities

1. Architect scalable AI/ML solutions by applying expertise in Python, TensorFlow, PyTorch, and scikit-learn to address complex business challenges and ensure robust model performance.
2. Design and optimize data pipelines using Apache Spark, Kafka, and Airflow, enabling efficient data ingestion, transformation, and real-time analytics across distributed environments.
3. Develop and validate machine learning models for NLP, time series forecasting, and deep learning tasks using pandas, NumPy, XGBoost, and Spark Mllib, ensuring accuracy and reliability.
4. Establish architectural standards and governance by reviewing solution designs and ensuring compliance with industry best practices and organizational guidelines.
5. Integrate and manage data storage solutions using PostgreSQL and MySQL, ensuring data integrity, scalability, and security within AI/ML platforms.
6. Mentor and train technical teams in advanced AI/ML methodologies, fostering skill development and mitigating delivery risks through knowledge sharing and capability building.
7. Evaluate emerging technologies and tools in AI/ML, recommending adoption strategies to maintain solution relevance and competitive advantage.
8. Architect and implement RESTful API integrations to enable seamless communication between AI/ML components and external systems, ensuring scalable, secure, and efficient data exchange across diverse enterprise environments.

Skill Requirements

1. Architectural Leadership In Ai/Ml, Demonstrating Crossdomain Expertise And Strategic Technical Direction.
2. Expert Proficiency In Python, Machine Learning Model Development, And Deep Learning Frameworks Such As Tensorflow And Pytorch.
3. Advanced Knowledge Of Data Engineering Tools Including Apache Spark, Kafka, And Airflow For Largescale Data Processing.
4. Solid Experience With Classical Ml, Nlp, Time Series Forecasting, And Model Validation Techniques.
5. Excellent Skills In Sql, Postgresql, And Mysql For Data Management And Integration.
6. Strong Understanding Of Cloudbased Ai/Ml Platforms Such As Databricks And Distributed Computing Environments.

Other Requirements

1. Certifications such as TensorFlow Developer Certificate
2. AWS Certified Machine Learning � Specialty
3. or Google Professional Machine Learning Engineer are optional but highly valuable for this position