Communication Skills: Excellent

Interpersonal Skills

Self-motivated- High

Confident- High

Leadership- High

Problem Solving, Logical, Analytical Skills

Problem Solving – High
Logical Ability: High

Analytical – High

Must Have Skills:

• Deep learning and machine learning
• Programming: Strong skills in Python, R, or Java
• AI Frameworks: Experience with TensorFlow, PyTorch, scikit-learn, Keras.
• Data Engineering: Knowledge of data pipelines, preprocessing, and storage
• Cloud Platforms: Familiarity with AWS AI/ML, Azure AI, Google Cloud AI, NLP, Deployment & Scalability:
• Ability to deploy and scale models in production, Data Governance: Awareness of data security and compliance.

Good to Have:

We are seeking a highly skilled and experienced AI Technical Architect to lead the design and implementation of AI-driven solutions within our organization. The AI Technical Architect will be responsible for defining the AI architecture, guiding development teams, and ensuring the successful delivery of AI projects. This role requires a deep understanding of artificial intelligence technologies, machine learning algorithms, and data analytics.

Roles and Responsibilities

• AI Solution Design: Collaborate with stakeholders to understand business requirements and design AI solutions that address complex problems and deliver business value.
• Architecture Development: Define and document the AI architecture, including data pipelines, model training and deployment strategies, and integration with existing system.
• Technical Leadership: Provide technical guidance and mentorship to AI engineers and data scientists, ensuring the adoption of best practices and adherence to architectural standards.
• Sprint Participation and Scrum Management from delivery standpoint
• Dev Management
• Technology Evaluation: Research and evaluate emerging AI technologies, tools, and frameworks to recommend optimal solutions for our projects.
• Data Strategy: Develop data strategies and data governance frameworks to ensure the quality, security, and privacy of data used in AI projects.
• Performance Optimization: Optimize AI models and algorithms for performance, scalability, and efficiency, leveraging techniques such as parallel processing and distributed computing.
• Collaboration: Collaborate with cross-functional teams, including data engineering, software development, and business intelligence, to integrate AI solutions into existing systems and workflows.
• Client Interaction: Engage with clients to understand their AI requirements, provide technical expertise and recommendations, and present solutions and proposals.