AI Technological Lead

Job Summary

This Platform Development Engineering role is responsible for the end-to-end development and maintenance of a robust and scalable software platform. The main purpose of this role is to create and maintain a platform that effectively supports a variety of applications and meets the needs of end-users globally. This includes designing and developing the platform architecture, writing and debugging code, and ensuring the platform's performance and reliability. Key areas of focus involve defining technical requirements, creating product roadmaps, and providing technical guidance and documentation. The role also requires expertise in software programming languages and database technologies to ensure the platform's functionality and scalability.

Key Responsibilities

• Platform Architecture and Design, Ensure the overall software platform architecture design meets business needs and is scalable and maintainable.
• Code Development and Implementation, Ensure code quality and adherence to standards across the platform development team.
• Platform Testing and Quality Assurance, Ensure platform quality and oversee testing processes to deliver a stable and reliable platform.
• Database Management and Optimization, Ensure database performance and data integrity across all platform operations.
• Technical Documentation and Guidance, Ensure documentation quality and completeness.
• Platform Maintenance and Support, Ensure platform stability and maintain optimal support response times to minimize disruptions.
• Roadmap Planning and Feature Enhancement, Define and prioritize product roadmaps for platform evolution, ensuring alignment with business goals and user needs.
  
  

Qualification:

• Minimum of 10+ years of hands-on experience in software/platform development, with at least 2 years in AI/ML-driven system design and implementation.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical field.
• Advanced certifications in AI/ML, Cloud Computing, or Enterprise Architecture (e.g., AWS Certified Solutions Architect, Google Professional ML Engineer, Microsoft Certified: Azure AI Engineer) are highly desirable.
• Preferred specialized coursework or research in Artificial Neural Networks (ANN), Deep Learning, Computational Neuroscience, or Bio-inspired AI.
• Certifications in AI/ML frameworks (e.g., TensorFlow, PyTorch) or cloud-based AI deployment (AWS, GCP, Azure).
• Proven track record in leading the development of scalable, high-performance AI platforms and managing end-to-end architecture.
• Expert-level proficiency in programming languages (Python, Java, C++, etc.), AI/ML frameworks (TensorFlow, PyTorch, etc.), and cloud platforms (AWS, Azure, GCP).
• Extensive experience in database management & optimization (SQL/NoSQL), system integration, and security administration for AI-powered platforms.
• Strong background in technology strategy formulation, roadmap planning, and stakeholder management to align AI solutions with business objectives.
• Leadership experience in guiding cross-functional teams, mentoring engineers, and driving innovation in AI/ML applications.
• Prior exposure to emerging AI technologies (Generative AI, NLP, Computer Vision) and their practical deployment in enterprise environments."
• Proven track record in leading AI/ML projects from concept to deployment, including algorithm development, model optimization, and performance tuning.
• Strong expertise in statistical analysis, big data processing, and distributed computing frameworks (e.g., Spark, Hadoop).
• Experience with large-scale neural network training, reinforcement learning, or biologically inspired AI models.
• Demonstrated ability in technical project management, ensuring alignment with business objectives and timelines.
• Experience in applying neural networks to real-world industry problems (e.g., robotics, autonomous systems, healthcare, finance).
• Prior work in research operations, technical product development, or AI innovation labs.
• Familiarity with edge AI deployment, neuromorphic computing, or spiking neural networks (SNNs).
• Experience in mentoring or leading cross-functional AI/ML teams.