AI Architect

Job Summary:

Key Responsibilities:

Product-Centric AI Architecture

• Design end-to-end AI architectures that support scalable, maintainable, and high-performance AI-driven products.
• Translate business and product requirements into concrete AI/ML solution blueprints.

Hands-On Product Development

• Lead development of AI models and components for product features - from PoC to production.
• Integrate models into product pipelines, ensuring performance, scalability, and reliability.
• Collaborate with engineering teams to productionize and optimize AI workflows (inference, retraining, monitoring, etc.).

Experimentation & Prototyping

• Build rapid prototypes and MVPs to validate hypotheses and test new product ideas.
• Use experimental data and feedback loops to iterate on model and feature design.

Collaboration with Product Teams

• Partner with product managers to define AI feature scope, feasibility, and timelines.
• Contribute to product roadmaps by identifying opportunities for AI-driven innovation.

Research-Driven Development

• Continuously evaluate new tools, models, frameworks, and research relevant to product needs.
• Apply the latest advancements in ML, NLP, computer vision, or generative AI where applicable.

Required Skills & Qualifications:

• 14-18 years in software/tech roles with strong exposure to AI/ML product development.
• Proven experience delivering production-grade AI features or platforms in commercial products.
• Deep understanding of machine learning, deep learning, NLP, or computer vision.
• Strong proficiency in Python and AI frameworks such as TensorFlow, PyTorch, Hugging Face, etc.
• Familiarity with cloud platforms (AWS/GCP/Azure) and MLOps practices for deploying and maintaining models.
• Experience working in agile product teams and collaborating cross-functionally with engineering, design, and product management.
• Ability to communicate technical decisions and trade-offs clearly to stakeholders.

Nice to Have:

• Experience in generative AI applications (e.g., LLMs, prompt engineering, fine-tuning).
• Exposure to A/B testing and data-driven product optimization.
• Understanding of responsible AI principles and model governance.