Model Development: Design, develop, and implement state-of-the-art generative AI models and open-source LLMs tailored to specific business needs.
NLP Expertise: Apply advanced NLP techniques to enhance model performance in tasks such as text generation, summarization, translation, and sentiment analysis.
Open-source Contribution: Actively contribute to and collaborate with the open-source community to improve and innovate on existing LLMs.
Data processing: Knowledge of data processing frameworks and libraries such as Apache Spark, PyTorch, Pandas, NumPy, Scikit-learn, and TensorFlow. Experience in preparing and processing data for machine learning workflows should be a consideration.
LLM Frameworks: Strong understanding of frameworks like LangChain or AutoGen for LLM-related candidates. Proficiency in leveraging these tools to build scalable and efficient solutions is essential.
Model finetuning: Expertise in fine-tuning pre-trained models for specific use cases. Candidates should be skilled in optimizing model performance and adapting them to unique datasets to deliver high-quality results.
Production ready applications: Expertise in frameworks such as FastAPI, Django, or similar tools. Candidates should demonstrate the ability to design, develop, and deploy machine learning models into a production environment.
MLOps Integration: Develop and maintain robust MLOps pipelines for seamless deployment, monitoring, and management of AI models in production environments.
Data Engineering: Collaborate with data engineers to ensure the availability of high-quality data for model training and validation.
Research & Innovation: Conduct research to stay abreast of the latest advancements in generative AI, NLP, and open-source LLMs, and integrate new techniques into existing workflows.
Performance Optimization: Optimize and fine-tune models for performance, scalability, and reliability, ensuring they meet production standards.
Technical Leadership: Mentor junior developers, provide technical guidance, and participate in code reviews to uphold best practices in software development.
Required Qualifications:
Bachelors or Masters degree in Computer Science, Engineering, or a related field.
4-5 years of hands-on experience in natural language processing and machine learning.
Proficiency in programming languages such as Python, R, or Java.
Extensive experience with NLP frameworks and libraries such as Hugging Face Transformers, spaCy, NLTK, and OpenAI GPT.
Strong understanding of algorithms, data structures, and software design principles.
Proven track record of deploying NLP models in production environments.
Experience with MLOps tools and practices, including CI/CD pipelines, Docker, Kubernetes, and model monitoring.
Excellent problem-solving skills and attention to detail.
Strong communication and teamwork skills.
Preferred Qualifications:
Experience with cloud platforms such as AWS, Google Cloud, or Azure.
Knowledge of big data technologies like Hadoop, Spark, or Kafka.