Senior AI Engineer

Duties and Responsibilities

• Lead efforts to leverage existing AI models and frameworks to solve complex business challenges. - Develop agentic orchestration by designing and implementing reason and act loops that allow AI agents to decompose complex goals into actionable sub-tasks. - Design and implement a versioned prompt registry allowing for model-agnostic routing and A/B testing of system prompts

• Engineer the privacy shield for PII redaction and the hallucination verifier to audit AI-generated actions before execution

• Conduct the full cycle of data modeling and algorithm development, including modeling, training, tuning, validating, deploying, and maintaining services, (AI breadth). - Strong domain expertise in the AI area including LLM, Time Series, RAG, fine-tune large models, traditional ML models, etc. (AI depth)

• Stay current with industry trends and advancements in data science and AI technologies. (State-of-the-Art)

• Perform data analysis and offer insights to inform business decisions across multiple domains. - Adhere to data privacy and security protocols to uphold the confidentiality of sensitive information. - Collaborate with cross-functional teams to understand requirements and translate them into effective AI and data science solutions. - Document and communicate technical designs, processes, and best practices to stakeholders using visualizations and presentations. - Take charge of projects, ensuring timely completion in a dynamic work environment. ### Qualifications and Experience

• Master’s or PhD in Computer Science, Engineering, Mathematics, Applied Sciences, or a related field preferred. Bachelor's degree required. - Strong programming skills in languages such as Golang, Python, Java, or C++, with hands-on experience in relevant frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Proven experience building multi-agent systems and frameworks with adaptive orchestration

• Deep understanding of Jailbreak Detection (e.g., LlamaGuard) and PII masking techniques to ensure safe LLM interactions in regulated environments

• In-depth knowledge and understanding of current machine learning algorithms, AI technologies, and platforms. - Solid experience in data preprocessing, feature engineering, and model evaluation techniques. - Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is a plus. - Strong knowledge of software development best practices, version control systems, and agile methodologies. - Results-driven with a positive can-do attitude and excellent problem-solving skills. - Exceptional verbal and written communication skills, with the ability to collaborate effectively with cross-functional teams. - Experience in the automotive industry is highly desirable.