Key Responsibilities:

1. AI Model Development:

   • Design and implement AI/ML models for predictive analytics, natural language processing, computer vision, or other domains.
   • Research state-of-the-art algorithms and techniques to improve model performance.
   • Perform data preprocessing, feature engineering, and model optimization.

2. Solution Deployment:

   • Develop end-to-end AI pipelines, including data ingestion, model training, evaluation, and deployment.
   • Implement scalable solutions on cloud platforms (e.g., AWS, Azure, GCP) or on-premises systems.

3. Collaboration and Leadership:

   • Collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into applications.
   • Mentor junior engineers and provide technical guidance.

4. Performance Optimization:

   • Monitor AI systems' performance and retrain models as needed to ensure accuracy and efficiency.
   • Implement strategies for model interpretability, robustness, and fairness.

5. Documentation and Reporting:

   • Document AI methodologies, workflows, and systems architecture.
   • Present findings and recommendations to stakeholders and leadership.

Qualifications:

Education:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related fields. A Ph.D. is a plus.

Experience:

• 2+ years of experience in AI/ML development, including deploying models in production environments.
• Hands-on experience with frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar.
• Experience with large-scale data processing using tools like Spark, Hadoop, or Kafka.

Technical Skills:

• Proficiency in Python, R, or Java; knowledge of C++ or Julia is a plus.
• Strong understanding of algorithms, data structures, and machine learning concepts.
• Familiarity with databases (SQL and NoSQL) and big data tools.
• Knowledge of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).