AI​/ML Architect

We are seeking a highly skilled AI/ML Architect to design, develop, and deploy machine learning models and AI-driven solutions that enhance business processes and product capabilities. The ideal candidate will have strong expertise in data science, algorithm development, and software engineering, with a passion for solving complex problems using cutting-edge technologies.

Key Responsibilities:

- Model Development:
Build, train, and optimize machine learning models for predictive analytics, natural language processing, computer vision, and other AI applications.
- Data Engineering:
Preprocess, clean, and structure large datasets for model training and evaluation.
- Deployment & Integration:
Implement ML models into production systems using scalable frameworks and APIs.
- Research &

Innovation: Stay updated with the latest AI/ML trends and incorporate best practices into solutions.
- Performance Monitoring:
Continuously monitor and improve model accuracy, efficiency, and robustness.

- Collaboration:

Work closely with cross-functional teams including software engineers, product managers, and data analysts to deliver AI-driven features.

Required Skills &

Qualifications:

- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
- 10 years of relevant experience
- Strong proficiency in Python, Tensor Flow, PyTorch, and ML libraries.
- Extensive experience designing and implementing AI/ML architectures in enterprise environments.

- Experience with data pipelines, feature engineering, and model deployment.
- Define and evolve the technical architecture for the modernization.
- Lead end-to-end solution design, ensuring scalability, performance, and security.
- Establish architectural standards, patterns, and best practices.
- Hands-on experience with AI/ML integration and intelligent agent frameworks.
- Knowledge of cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes).
- Excellent problem-solving and analytical skills.

Preferred

Skills:

- Experience with deep learning, transformer models, and generative AI.
- Understanding of big data technologies (Spark, Hadoop).
- Exposure to AI ethics, bias mitigation, and responsible AI practices.