GenAI Engineer

Brief Job Description:

1. Generative AI Solution Development:
   • Develop and implement advanced solutions using Generative AI technologies, leveraging frameworks such as Python, LangChain, and HuggingFace.
   • Engage in prompt engineering, working with Large Language Models (LLMs) to create tailored AI-driven solutions.
   • Ensure response accuracy of prompts. Make sure the prompts generate desired outcomes and adhere to instructions.
2. Model Fine-Tuning and Optimization:
   • Fine-tune pre-trained models from HuggingFace and other platforms to meet project-specific requirements.
   • Optimize AI models for efficiency, accuracy, and scalability in educational contexts.
   • Achieve minimum accuracy thresholds for models.
   • Reduce model training time and computational resource usage.
   • Innovate AI techniques or algorithms at least once per quarter.
3. Collaboration with Business Teams:
   • Collaborate with business stakeholders to align AI solutions with organizational goals, translating business requirements into technical specifications.
   • Participate in cross-functional collaboration meetings, ensuring effective communication of AI model capabilities.
4. AI Frameworks and Tooling:
   • Utilize frameworks like LangChain, Cohere, Keras, TensorFlow, and PyTorch for developing, testing, and deploying AI models.
   • Integrate AI solutions into existing systems for seamless user experiences.
   • Ensure AI deployment times are within defined timelines (e.g., from training to production).
   • Maintain model latency below a defined threshold for real-time inference (e.g., under decided milliseconds).
5. Research and Innovation:
   • Stay updated on the latest advancements in Generative AI, machine learning, and deep learning technologies.
   • Experiment with new methodologies and emerging AI technologies to innovate and enhance existing solutions.
   • Introduce innovative AI algorithms or approaches at least once per quarter.
6. System Architecture and Integration:
   • Design robust system architectures using frameworks like FastAPI and Unicorn to support AI-driven applications.
   • Ensure AI solutions are scalable, maintainable, and integrated effectively with other system components.
   • Ensure efficient use of computing resources and optimize costs while maintaining performance standards.
7. Performance Monitoring and Improvement:
   • Monitor AI model performance, applying ML engineering metrics to continuously improve outcomes.
   • Troubleshoot issues related to model performance and integration, resolving critical bugs quickly.
   • Reductions in code execution time and model bugs.
   • Ensure AI-related code has unit testing coverage for robustness.
8. Documentation:
   • Provide comprehensive documentation for all developed models, data pipelines, and processes, explaining functionality and edge cases.
   • Maintain detailed documentation of new features and code implementations.
   • Ensure step-by-step deployment and retraining documentation, with minimal user-reported gaps or confusion.

• Education: Bachelor''s degree or equivalent experience in Computer Science, AI, or related fields.

• Experience: 3-5 years of experience, with at least 1 year specializing in AI/ML.

• Prompt Engineering

• Python Programming

• AI and ML Frameworks

• Deep Learning Architectures

• Mathematical and Statistical Knowledge

• System Architecture Design

• Model Fine-Tuning

• Collaboration and Communication

• Continuous Learning