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Technical Lead

3 months ago

Ahmedabad, Gujarat, India Crest Data Full time

The Technical Lead for AI/ML plays a critical role in driving the technical direction and execution of artificial intelligence and machine learning projects within the organization. This position combines strong technical expertise in AI/ML with leadership skills to guide a team of engineers and data scientists in developing innovative AI/ML solutions. The Technical Lead will collaborate closely with cross-functional teams to translate business requirements into technical designs and drive the successful implementation, testing and deployment of AI/ML models and systems.

Key Responsibilities:

1. AI/ML Model Training and Development

  • Lead the development of new AI Models from scratch and fine-tuning existing base models with new data
  • Implement and execute model evaluation experiments to test and pick the best model for a certain task
  • Build high throughput and automated data pipelines to allow Data analysis and model training
  • Lead feature engineering to select the most relevant features in datasets to train models on
  • Pick the right set of evaluation metric to test model candidates for deployment
  • Use MLOps principles to allow continuous model tuning and performance improvements

2. AI/ML Model Deployment:

  • Lead the deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance.
  • Implement best practices for model versioning, monitoring, and maintenance to ensure ongoing model accuracy and effectiveness.
  • Collaborate with DevOps and infrastructure teams to integrate AI/ML components into CI/CD pipelines and automated deployment processes.
  • Implement CI/CD practices for AI/ML development, including automated testing, code review processes, and continuous integration pipelines.
  • Automate deployment processes for AI/ML models using tools such as Jenkins, GitLab CI/CD, or similar platforms.

3. Technology Expertise:

  • Demonstrate deep expertise in AI/ML technologies, including TensorFlow, PyTorch, Keras, NumPy, Pandas and familiarity with platforms such as OpenAI, Hugging Face, Perplexity AI and Anthropic.
  • Stay current with advancements in AI/ML research and technologies, evaluating their applicability to the organization's needs and projects.

4. Architecture and Design:

  • Design and implement architectures around AI/ML solutions, including data pipelines, model serving infrastructure, and integration with existing systems.
  • Collaborate with data engineers to ensure the availability, quality, and reliability of data sources for AI/ML model training and deployment.

5. Python Development:

  • Utilize Python programming for AI/ML model development, automation scripts, and development of supporting tools and utilities.
  • Collaborate with software engineering teams to integrate AI/ML capabilities into software applications and services.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field.
  • Extensive experience (6+ years) in AI/ML development, with a focus on deploying models into production environments.
  • Strong proficiency in AI/ML frameworks such as TensorFlow, PyTorch, Keras, NumPy, Pandas and familiarity with platforms such as OpenAI, Hugging Face, Perplexity AI and Anthropic.
  • Experience building architectures around AI/ML solutions, including data pipelines, model serving infrastructure, and integration with existing systems.
  • Hands-on experience with CI/CD practices and tools, with a strong understanding of software development lifecycle processes.
  • Proficiency in Python programming and experience with relevant libraries and frameworks for AI/ML development. Experience of Python Pandas and similar languages is a must
  • Worked on pre-processing pipelines ensuring security compliances standards are met
  • Excellent communication skills and the ability to collaborate effectively with cross-functional teams.
  • Strong problem-solving abilities and a passion for innovation and continuous improvement in AI/ML deployment practices.