Key Responsibilities

• AI Solution Architecture – Design and oversee the implementation of AI/ML models, ensuring scalability, security, and efficiency.
• Model Deployment & Integration – Work with DevOps and MLOps teams to deploy AI solutions in cloud, edge, or hybrid environments.
• Cloud & Infrastructure Management – Architect AI solutions using AWS, Azure, GCP, or on-premise infrastructure.
• Data Pipeline Optimization – Ensure seamless data ingestion, preprocessing, and storage for AI models.
• Performance Tuning & Monitoring – Optimize AI workflows and continuously monitor model performance.
• Security & Compliance – Implement best practices to secure AI applications and ensure compliance with industry regulations.
• Collaboration with Teams – Work closely with data scientists, engineers, and business leaders to align AI solutions with business objectives.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field
• 5+ years of experience in AI/ML architecture, cloud computing, or data engineering
• Strong programming skills in Python, Java, or Scala
• Expertise in ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Proficiency in cloud platforms (AWS SageMaker, Azure ML, GCP AI Platform)
• Experience with MLOps, CI/CD, Kubernetes, Docker, and model monitoring tools
• Strong understanding of data pipelines, ETL, and big data processing (Apache Spark, Kafka, Snowflake)

Preferred Qualifications

• Certifications in AWS Certified Machine Learning, Google Professional ML Engineer, or Microsoft Azure AI Engineer
• Experience in AutoML, Edge AI, and NLP-based AI solutions
• Knowledge of ethical AI principles and compliance standards (GDPR, HIPAA, etc.)

Job Benefits

• Competitive salary with performance-based incentives
• Opportunity to work with cutting-edge AI/ML technologies
• Flexible work environment (remote/hybrid)
• Career growth opportunities and continuous learning programs