Technical Lead

5 days ago

Faridabad, India CRPTM Full time

A Tech Lead in a product startup plays a crucial role in driving the technical direction, ensuring code quality, mentoring the team, and aligning technology with business goals. Unlike in larger enterprises, a startup Tech Lead often wears multiple hats, balancing hands-on coding, architecture decisions, and leadership responsibilities.Roles & Responsibilities of a Tech Lead in an AI-Driven Product Startup1. AI Strategy & Technical 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, LangChain, OpenAI APIs).Ensure AI solutions are scalable, efficient, and ethical in their application.2. 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 .3. 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.4. 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.5. Product & Business Alignment-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 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 .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 .

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