Lead Machine Learning Engineer

Position: Lead Machine Learning Engineer
Location: Remote
Duration: 6 Months C2H

As a Senior or Lead Machine Learning Engineer on our Applied AI team, you will operate at the frontier of AI-driven innovation. You will design, fine-tune, and implement state-of-the-art language model applications and machine learning systems, integrating AI-powered solutions into client's platform for fraud detection, decision automation, and process optimization. This role requires a blend of technical leadership, hands-on engineering expertise, and a genuine passion for deploying AI and ML models at scale. You will be instrumental in building, scaling, and governing AI systems ensuring robust performance, explainability, and compliance in high-stakes, real-world applications.

What You'll Contribute
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.

What We're Seeking
Experience: 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.
ML Foundations: Deep expertise in ML algorithms, deep learning architectures, and the underlying mathematical foundations particularly linear algebra, probability, and statistics.
Data & Pipelines: Proven proficiency in working with large-scale datasets and building efficient, reliable AI data pipelines.
Model Deployment: Hands-on experience packaging and deploying ML models as APIs for seamless integration into production environments.
MLOps: Familiarity with MLOps tooling and platforms such as MLflow, Azure ML, or Vertex AI, with an understanding of model lifecycle management.
Cloud-Native AI: Experience with cloud-native AI architectures, including distributed model training and scalable deployment patterns on AWS, Google Cloud Platform, or Azure.
LLM Expertise: Strong background in LLM architectures, prompt engineering, fine-tuning, model adaptation, and RAG techniques.
Evaluation & Testing: Robust understanding of AI evaluation methodologies, testing frameworks, and A/B testing for AI-driven applications.
Deep Learning Frameworks: Proficiency with PyTorch, JAX, or TensorFlow.
Vector Databases & Monitoring: Knowledge of vector databases (e.g., Pinecone, Weaviate, pgvector) and AI model monitoring practices including drift detection and governance.
Software Engineering: Strong software engineering fundamentals, with demonstrated ability to write clean, maintainable, and production-quality AI code.
Leadership: Experience mentoring engineering teams and driving AI adoption across cross-functional groups.
Research Contributions: Publications, patents, or open-source contributions in AI/ML are a plus.
Education: Bachelor's, Master's, or PhD in Computer Science, a related field, or equivalent practical experience, with a focus on machine learning.

Thanks & Regards,

Bhupender Singh

XL Impex Inc dba

Atika Technologies

5 Independence Way, Suite 300,

Princeton, NJ 08540