Associate Data Scientist

Credence is seeking a talented and motivated Engineer to join our growing team. In this role, you will design, develop, and implement AI models and machine learning algorithms to support a variety of high-impact projects. This position is ideal for an engineer ready to deepen their expertise and take on exciting technical challenges.

• Design, develop, and deploy machine learning models and AI-driven solutions. 

• Collaborate with cross-functional teams, including data scientists, software engineers, product managers, and client stakeholders to understand, evaluate and deliver AI solutions that meet the requirements. 

• Conduct data preparation, feature engineering, model selection, training and optimization to ensure optimal performance from the AI models. 

• Design and implement AI solutions using the latest Generative AI technologies and foundation models / large-language models (LLMs). 

• Develop automation scripts for MLOps pipelines in cloud using Infrastructure as Code (IaC) for ML model deployment in model inferencing workflows, following best practices of model versioning and CI/CD deployments. 

• Monitor and maintain AI models post-deployment, ensuring performance, accuracy, and scalability. 

• Contribute to the development of AI tools, frameworks, and best practices to support the company’s AI initiatives. 

• Stay up-to-date with emerging trends, tools, and techniques in AI and machine learning. 

• Write clean, maintainable, and well-documented code following industry standards. 

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. 

• 3-5 years of hands-on experience in AI/ML development in professional working environment, with a track record of successful AI project deployments. 

• Strong understanding of supervised, unsupervised, and reinforcement learning techniques. 

• Proficiency in using Python and common AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas) for data preprocessing, feature engineering, and model development techniques. 

• Basic understanding and usage of foundation models / large language models (LLMs), vector embeddings and their application. 

• Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes). 

• Experience using ML frameworks and tools in the cloud, such as Amazon Sagemaker. 

• Strong problem-solving skills and the ability to work both independently and as part of a team. 

• Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders. 

Preferred Qualifications 

• Experience with Natural Language Processing (NLP). 

• Experience using advanced search and retrieval techniques like retrieval augmented generation (RAG) for Large Language Models (LLMs). 

• Experience with fine tuning of Large Language Model with custom data sets. 

• Familiarity with MLOps principles and tools such as MLflow. 

• Knowledge of software development best practices, including version control (Git) and CI/CD pipelines. 

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