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Senior Artificial Intelligence Engineer

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Haryana, India beBeeArtificialintelligence Full time ₹ 1,20,00,000 - ₹ 1,60,00,000
AI Technical Lead

A strategic AI Technical Lead plays a pivotal role in driving the technological direction, ensuring code quality, and mentoring a team of engineers. Unlike larger enterprises, a startup AI Technical Lead often wears multiple hats, balancing hands-on coding, architecture decisions, and leadership responsibilities.

Main Responsibilities of an AI Technical Lead
  • 1. Strategic AI Architecture & Leadership
    • Define and drive the AI architecture and technical strategy aligned with business goals.
    • Select the right AI/ML frameworks, models, and tools (e.g., TensorFlow, PyTorch).
    • Ensure AI solutions are scalable, efficient, and ethical in their application.
  • 2. Hands-on AI Development & Software Engineering
    • Contribute to core AI model development and deployment.
    • Design and optimize data pipelines for model training and inference.
    • Integrate AI models with the backend, APIs, and user-facing applications.
  • 3. AI Infrastructure & DevOps Engineering
    • Implement MLOps for continuous integration and deployment of AI models.
    • Manage cloud-based AI services (AWS SageMaker, GCP Vertex AI).
    • Ensure efficient GPU/TPU utilization for model training and inference.
  • 4. Team Mentoring & AI Engineering Best Practices
    • Mentor engineers on AI best practices, model tuning, and algorithm selection.
    • Conduct AI-focused code reviews and model evaluations.
    • Foster a data-driven culture and encourage innovation in AI applications.
  • 5. Product Alignment & Business Strategy
    • Work closely with founders, product managers, and data scientists to align AI capabilities with product vision.
    • Evaluate AI use cases for improving customer experience and operational efficiency.
    • Balance innovation with practical AI implementation to avoid over-engineering.
  • 6. Data Governance & Model Compliance
    • Define data collection, annotation, and storage strategies.
    • Ensure AI models comply with data privacy laws.
    • Address issues like AI bias, explainability, and fairness.
  • 7. AI Performance Optimization & Scalability
    • Optimize models for low latency inference and real-time processing.
    • Leverage techniques like model compression, quantization, and pruning to reduce costs.
    • Implement A/B testing and monitoring to continuously improve AI models.
  • 8. AI Ethics & Responsible AI Development
    • Ensure AI solutions are transparent, unbiased, and explainable.
    • Mitigate risks associated with hallucinations, data drift, and adversarial attacks.
    • Stay updated with ethical AI guidelines and emerging industry regulation.