Technical Lead

4 days ago

Rohtak, 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 Startup

1. 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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