Job Summary

This role is responsible for architecting and designing advanced artificial intelligence and machine learning solutions, leveraging a broad suite of data engineering and modeling technologies. The position drives strategic technical decisions, guides teams in implementing scalable AI/ML systems, and ensures alignment with organizational standards and client requirements. They play a pivotal role in integrating modern data platforms and delivering innovative solutions that address complex business challenges.

Key Responsibilities

1. Architect end-to-end AI/ML solutions using Python, TensorFlow, PyTorch, and scikit-learn, ensuring robust model development and deployment frameworks.
2. Design scalable data pipelines and real-time processing systems utilizing Apache Spark, Kafka, and PostgreSQL to support machine learning workflows.
3. Guide the team in implementing advanced ML models, including deep learning, NLP, and time series forecasting, using tools such as XGBoost, LightGBM, and Spark MLlib.
4. Oversee integration of data engineering platforms like Apache Airflow, DataBricks, and RabbitMQ to optimize data ingestion, transformation, and orchestration for AI/ML projects.
5. Ensure technical excellence by advocating best practices in model validation, performance optimization, and reproducibility across Python, R, and SQL-based environments.
6. Collaborate with stakeholders to gather requirements, translate business needs into technical specifications, and deliver tailored AI/ML solutions that meet quality and compliance standards.
7. Mentor and coach team members in advanced AI/ML concepts, fostering continuous learning and adoption of emerging technologies within the skill cluster.
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. Expert Proficiency In Ai/Ml Model Development, Including Classical Machine Learning, Deep Learning, Nlp, And Time Series Forecasting.
2. Excellent Knowledge Of Python, R, Sql, And Bash For Data Analysis, Modeling, And Automation.
3. Expertlevel Experience With Tensorflow, Pytorch, Scikitlearn, Pandas, Numpy, Xgboost, Lightgbm, And Spark Mllib For Building And Deploying Models.
4. Advanced Proficiency In Designing And Managing Data Pipelines Using Apache Spark, Kafka, Airflow, Databricks, And Rabbitmq.
5. Excellent Understanding Of Relational Databases Such As Postgresql And Mysql For Data Storage And Retrieval.
6. Strong Ability To Translate Business Requirements Into Technical Solutions And Deliver Highcomplexity Ai/Ml Architectures.

Other Requirements

1. Optional But Valuable:
2. Google Professional Machine Learning Engineer
3. - Aws Certified Machine Learning � Specialty
4. - Optional: Databricks Certified Machine Learning Professiona