LLM Engineer
Budget: ₹37,500 – ₹75,000 INR
Core Development : Spearhead the design, development, and deployment of generative AI models with a focus on natural language understanding and processing.
Tool Utilization and Contribution : Engage with and contribute to libraries and frameworks such as LangChain, ensuring our application development is as efficient as it is innovative.
Collaboration : Ensure a smooth partnership with Data Engineering and Software Engineering teams for flawless integration of AI models into production systems.
Continuous Learning : Keep up with the rapid advancements in AI/ML, including emerging tools, libraries, and best practices, applying this knowledge to propel our projects forward.
Ethics in AI : Champion ethical AI development, prioritizing fairness, unbiased outcomes, and privacy.
Machine Learning and NLP Expertise : Your background in machine learning and deep learning, particularly in neural networks and NLP, sets the foundation for success.
Technical Proficiency : With Python fluency and familiarity with AI/ML libraries and tools, including LangChain, OpenAIs GPT, TensorFlow, and PyTorch, you re equipped to innovate.
Distributed Computing : Experience with distributed computing for AI is essential, as is the ability to navigate the complexities of GPU or TPU utilization.
Software Engineering Know-How : A strong grasp of software engineering practices, from version control to CI/CD pipelines, ensures quality and efficiency.
Problem-Solving and Collaboration : Exceptional problem-solving skills, innovative thinking, and the ability to collaborate effectively across teams are crucial.
Ethical Framework : A solid understanding of ethical AI principles underpins everything we do.
Tool Competency : Proficiency in Google Colab and Postman or similar API platforms is vital for prototyping and testing AI models.
Tool Utilization and Contribution : Engage with and contribute to libraries and frameworks such as LangChain, ensuring our application development is as efficient as it is innovative.
Collaboration : Ensure a smooth partnership with Data Engineering and Software Engineering teams for flawless integration of AI models into production systems.
Continuous Learning : Keep up with the rapid advancements in AI/ML, including emerging tools, libraries, and best practices, applying this knowledge to propel our projects forward.
Ethics in AI : Champion ethical AI development, prioritizing fairness, unbiased outcomes, and privacy.
Machine Learning and NLP Expertise : Your background in machine learning and deep learning, particularly in neural networks and NLP, sets the foundation for success.
Technical Proficiency : With Python fluency and familiarity with AI/ML libraries and tools, including LangChain, OpenAIs GPT, TensorFlow, and PyTorch, you re equipped to innovate.
Distributed Computing : Experience with distributed computing for AI is essential, as is the ability to navigate the complexities of GPU or TPU utilization.
Software Engineering Know-How : A strong grasp of software engineering practices, from version control to CI/CD pipelines, ensures quality and efficiency.
Problem-Solving and Collaboration : Exceptional problem-solving skills, innovative thinking, and the ability to collaborate effectively across teams are crucial.
Ethical Framework : A solid understanding of ethical AI principles underpins everything we do.
Tool Competency : Proficiency in Google Colab and Postman or similar API platforms is vital for prototyping and testing AI models.