Technical Lead

0 years

0 Lacs

Posted:13 hours ago| Platform: Linkedin logo

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Job Type

Full Time

Job Description

Tech Lead


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 regulations

    .

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