Nerdleveltech is hiring: [Remote] Senior/ Lead - ML Engineer - Applied AI in Boz
Nerdleveltech, Bozeman, MT, United States
Note: The job is a remote job and is open to candidates in USA. FICO is a leading analytics and decision management company that empowers businesses and individuals around the world with data-driven insights. As a Senior or Lead Machine Learning Engineer on the Applied AI team, you will design and implement AI-driven solutions for fraud detection and decision automation, ensuring robust performance and compliance in real-world applications.
Responsibilities
Develop and implement LLM-powered solutions for decision automation, fraud investigation, and workflow optimization within FICO's platform
Engineer sophisticated prompting strategies and Retrieval-Augmented Generation (RAG) architectures tailored for enterprise-grade, high-stakes AI applications
Apply fine-tuning techniques, in-context learning, and custom evaluation frameworks to continuously optimize model performance
Design, test, and deploy new AI models, architectures, and training methodologies to keep FICO at the forefront of applied AI innovation
Develop comprehensive backtesting methodologies to validate model reliability, robustness, and predictive performance
Monitor deployed models in production, proactively detecting and responding to drift to maintain accuracy, fairness, and compliance over time
Research and integrate emerging AI techniques to drive continuous advancements in machine learning, reasoning, and decision automation
Write high-quality, production-ready code that ensures scalability, security, and operational integrity of deployed AI systems
Mentor and guide junior and mid-level engineers, promoting engineering best practices and fostering a culture of technical excellence
Qualifications
5+ years of hands-on experience in machine learning engineering, with a strong track record delivering large-scale AI/ML systems from research to production
Deep expertise in ML algorithms, deep learning architectures, and the underlying mathematical foundations – particularly linear algebra, probability, and statistics
Proven proficiency in working with large-scale datasets and building efficient, reliable AI data pipelines
Hands‑on experience packaging and deploying ML models as APIs for seamless integration into production environments
Familiarity with MLOps tooling and platforms such as MLflow, Azure ML, or Vertex AI, with an understanding of model lifecycle management
Experience with cloud-native AI architectures, including distributed model training and scalable deployment patterns on AWS, GCP, or Azure
Strong background in LLM architectures, prompt engineering, fine‑tuning, model adaptation, and RAG techniques
Robust understanding of AI evaluation methodologies, testing frameworks, and A/B testing for AI‑driven applications
Proficiency with PyTorch, JAX, or TensorFlow
Knowledge of vector databases (e.g., Pinecone, Weaviate, pgvector) and AI model monitoring practices including drift detection and governance
Strong software engineering fundamentals, with demonstrated ability to write clean, maintainable, and production‑quality AI code
Experience mentoring engineering teams and driving AI adoption across cross‑functional groups
Bachelor's, Master's, or PhD in Computer Science, a related field, or equivalent practical experience, with a focus on machine learning
Publications, patents, or open-source contributions in AI/ML are a plus
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Responsibilities
Develop and implement LLM-powered solutions for decision automation, fraud investigation, and workflow optimization within FICO's platform
Engineer sophisticated prompting strategies and Retrieval-Augmented Generation (RAG) architectures tailored for enterprise-grade, high-stakes AI applications
Apply fine-tuning techniques, in-context learning, and custom evaluation frameworks to continuously optimize model performance
Design, test, and deploy new AI models, architectures, and training methodologies to keep FICO at the forefront of applied AI innovation
Develop comprehensive backtesting methodologies to validate model reliability, robustness, and predictive performance
Monitor deployed models in production, proactively detecting and responding to drift to maintain accuracy, fairness, and compliance over time
Research and integrate emerging AI techniques to drive continuous advancements in machine learning, reasoning, and decision automation
Write high-quality, production-ready code that ensures scalability, security, and operational integrity of deployed AI systems
Mentor and guide junior and mid-level engineers, promoting engineering best practices and fostering a culture of technical excellence
Qualifications
5+ years of hands-on experience in machine learning engineering, with a strong track record delivering large-scale AI/ML systems from research to production
Deep expertise in ML algorithms, deep learning architectures, and the underlying mathematical foundations – particularly linear algebra, probability, and statistics
Proven proficiency in working with large-scale datasets and building efficient, reliable AI data pipelines
Hands‑on experience packaging and deploying ML models as APIs for seamless integration into production environments
Familiarity with MLOps tooling and platforms such as MLflow, Azure ML, or Vertex AI, with an understanding of model lifecycle management
Experience with cloud-native AI architectures, including distributed model training and scalable deployment patterns on AWS, GCP, or Azure
Strong background in LLM architectures, prompt engineering, fine‑tuning, model adaptation, and RAG techniques
Robust understanding of AI evaluation methodologies, testing frameworks, and A/B testing for AI‑driven applications
Proficiency with PyTorch, JAX, or TensorFlow
Knowledge of vector databases (e.g., Pinecone, Weaviate, pgvector) and AI model monitoring practices including drift detection and governance
Strong software engineering fundamentals, with demonstrated ability to write clean, maintainable, and production‑quality AI code
Experience mentoring engineering teams and driving AI adoption across cross‑functional groups
Bachelor's, Master's, or PhD in Computer Science, a related field, or equivalent practical experience, with a focus on machine learning
Publications, patents, or open-source contributions in AI/ML are a plus
#J-18808-Ljbffr