Data Scientist - Artificial Intelligence/Machine Learning

As an AI/ML Engineer, your primary responsibility will be to collaborate effectively with cross-functional teams, including data scientists and product managers. Together, you will work on acquiring, processing, and managing data for the integration and optimization of AI/ML models. Your role will involve designing and implementing robust, scalable data pipelines to support cutting-edge AI/ML models. Additionally, you will be responsible for debugging, optimizing, and enhancing machine learning models to ensure quality assurance and performance improvements. Operating container orchestration platforms like Kubernetes with advanced configurations and service mesh implementations for scalable ML workload deployments will be a key part of your job. You will also design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. Engaging in advanced prompt engineering and fine-tuning of large language models (LLMs) will be crucial, with a focus on semantic retrieval and chatbot development. Documentation will be an essential aspect of your work, involving the recording of model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. Researching and implementing cutting-edge LLM optimization techniques such as quantization and knowledge distillation will be part of your ongoing tasks to ensure efficient model performance and reduced computational costs. Collaborating closely with stakeholders to develop innovative natural language processing solutions, with a specialization in text classification, sentiment analysis, and topic modeling, will be another significant aspect of your role. Staying up-to-date with industry trends and advancements in AI technologies and integrating new methodologies and frameworks to continually enhance the AI engineering function will also be expected of you. In terms of qualifications, a Bachelor's degree in any Engineering stream is required, along with a minimum of 4+ years of relevant experience in AI. Proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) is essential. Experience with LLM frameworks, big data processing using Spark, version control, and experiment tracking, as well as proficiency in software engineering and development, DevOps, infrastructure, cloud services, and LLM project experience are also necessary. Your expertise should include a strong mathematical foundation in statistics, probability, linear algebra, and optimization, along with a deep understanding of ML and LLM development lifecycle. Additionally, you should have expertise in feature engineering, embedding optimization, dimensionality reduction, A/B testing, experimental design, statistical hypothesis testing, RAG systems, vector databases, semantic search implementation, and LLM optimization techniques. Strong analytical thinking, excellent communication skills, experience translating business requirements into data science solutions, project management skills, collaboration abilities, dedication to staying current with the latest ML research, and the ability to mentor and share knowledge with team members are essential competencies for this role.,