Senior Lead Applied AI Scientist
New
L
LumenApplied AI
Remote, US; Source API remote eligibility restrictions: United StatesFull-TimeLead
Salary176,310 - 242,425 USD per year
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Job Details
- Experience
- Seven plus years of experience in applied AI, machine learning, or data science, including production AI deployments.
- Required Skills
- PythonMachine LearningMLOpsGenerative AI
Requirements
- Bring seven plus years of experience in applied AI, machine learning, or data science, including production AI deployments.
- Demonstrate a track record of delivering AI solutions that drive measurable business outcomes.
- Have deep expertise in machine learning, statistics, experimentation, predictive analytics, and modern AI techniques.
- Bring hands-on experience building LLM, RAG, agentic, and machine learning solutions.
- Have experience with AI evaluation, model performance optimization, responsible AI, and production monitoring.
- Be proficient in Python and modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or MLflow.
- Have experience with MLOps, LLMOps, cloud AI platforms, APIs, and software engineering best practices.
- Preferred: Master's degree or PhD in Computer Science, AI, Machine Learning, Statistics, or a related quantitative field.
- Preferred: One plus years of experience building agent based solutions.
- Preferred: Experience operating enterprise-scale AI solutions in regulated or security-sensitive environments and leading complex cross-functional AI initiatives.
Responsibilities
- Design, build, and optimize AI solutions from concept through production deployment.
- Partner with business and product teams to identify opportunities, define success metrics, and validate value through rapid experimentation.
- Develop AI systems using machine learning, generative AI, retrieval, predictive modeling, and agentic architectures.
- Define evaluation frameworks and continuously improve quality, reliability, safety, performance, and cost.
- Partner with Engineering to deliver scalable, production-ready solutions using MLOps and LLMOps best practices.
- Contribute reusable frameworks, evaluation assets, and technical standards that accelerate enterprise AI adoption.
- Provide technical leadership, mentorship, and guidance on complex AI initiatives.
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