Lead AI Solutions Engineer

2 days ago


Mysore, Karnataka, India beBeeInnovation Full time ₹ 15 - ₹ 30

About the Role

We are seeking an experienced Artificial Intelligence Solutions Architect to join our team.

The ideal candidate will be responsible for designing and implementing scalable and production-ready AI solutions. To be effective in this position, you must feel comfortable owning the entire AI development process from inception to completion.

Responsibilities:

  • Lead and manage the AI development team to deliver high-quality and innovative solutions.
  • Conduct research to determine the best AI models and solutions for integration into our ecosystem and existing products.
  • Develop and oversee proofs of concept (PoCs) to validate AI-driven capabilities and business value.
  • Provide technical leadership and mentorship to team members across AI and software development functions.
  • Collaborate with stakeholders to align AI initiatives with overall product vision and business strategy.
  • Drive recruitment, onboarding, and continuous training of AI and engineering talent to build a high-performance team aligned with organizational goals.
  • Define the AI architecture and technical roadmap, ensuring seamless integration into cloud-based platforms and existing systems.
  • Own end-to-end lifecycle of AI feature development—from ideation, design, and prototyping to deployment and performance tuning.
  • Establish MLOps practices for continuous training, evaluation, deployment, and monitoring of AI models.
  • Ensure compliance with data privacy regulations, ethical AI practices, and internal security standards.

Qualifications:

  • Bachelor's degree in Computer Science, Engineering, or Mathematics
  • 5+ years' of relevant experience
  • Advanced knowledge of leading architecture solutions in the industry area
  • Strong interpersonal and collaboration skills
  • Ability to demonstrate technical concepts to non-technical audiences

Key Skills:

  • AI/ML model development and deployment
  • Cloud-based platform architecture
  • MLOps and DevOps practices
  • Data privacy and security
  • Technical leadership and mentoring

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