Senior Technologist

The role of a Technical Leader in a product startup is multifaceted, encompassing the direction of technical initiatives, ensuring code quality, mentoring team members, and aligning technology with business objectives. Unlike larger enterprises, a startup's Tech Lead often assumes multiple responsibilities, balancing hands-on coding, architectural decisions, and leadership duties.

• AI Strategy & Technical Leadership:
  • Develop and drive AI architecture and technical strategy aligned with business goals.
  • Choose the right AI/ML frameworks, models, and tools (e.g., TensorFlow, PyTorch, LangChain, OpenAI APIs).
  • Ensure AI solutions are scalable, efficient, and ethical in their application.
• Hands-on AI & Software Development:
  • 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.
• Ai Infrastructure & DevOps:
  • Implement MLOps for continuous integration and deployment of AI models.
  • Manage cloud-based AI services (AWS SageMaker, GCP Vertex AI, Azure AI).
  • Ensure efficient GPU/TPU utilization for model training and inference.
• Team Leadership & Ai Engineering Mentorship:
  • 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.
• Product & Business Alignment:
  • Collaborate 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.
• Data Strategy & Ai Model Governance:
  • Define data collection, annotation, and storage strategies.
  • Ensure AI models comply with data privacy laws (GDPR, CCPA).
  • Address issues like AI bias, explainability, and fairness.
• 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.
• 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.