Credence company
Associate Data Scientist
Credence company, Mc Lean, Virginia, us, 22107
Associate Data Scientist
Job Locations
US-VA-McLean
ID
2024-8913
Category
Information Technology
Type
Regular Full-Time
Overview
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. Responsibilities include, but are not limited to the duties listed below
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.
Education, Requirements and Qualifications
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.
Working Conditions and Physical Requirements
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Job Locations
US-VA-McLean
ID
2024-8913
Category
Information Technology
Type
Regular Full-Time
Overview
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. Responsibilities include, but are not limited to the duties listed below
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.
Education, Requirements and Qualifications
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.
Working Conditions and Physical Requirements
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We can recommend jobs specifically for you! Click here to get started.