Sr. Principal Machine Learning Engineer

Job Summary

You will work closely with product managers, software engineers, data scientists, architects, and business stakeholders to develop production-ready AI systems leveraging machine learning, deep learning, large language models, retrieval-augmented generation, Small Language Models, intelligent agents, and modern MLOps practices.

This is a senior individual contributor role requiring deep technical expertise, strong architectural thinking, and the ability to influence engineering practices across teams. The successful candidate will be expected to build scalable solutions, establish engineering best practices, mentor peers, and drive adoption of modern AI and machine learning capabilities.

This role is ideal for an engineer who enjoys solving complex business problems through innovative AI technologies while maintaining a strong focus on scalability, reliability, governance, and user value.

Key Responsibilities

AI/ML Solution Architecture and Engineering

• Architect and build production-grade machine learning, deep learning, generative AI, and agentic AI systems for commercial and field engagement use cases.
• Lead technical design for solutions involving retrieval-augmented generation, embeddings, semantic search, LLM orchestration, Text2SQL, recommendation systems, predictive modeling, and intelligent workflow automation.
• Design scalable AI services and APIs that integrate with enterprise data platforms, business applications, CRM ecosystems, and downstream product workflows.
• Design, train, fine-tune, and deploy neural network models for domain-specific commercial and field use cases, including deep learning architectures such as Transformers, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and LSTMs as applicable.
• Develop deep learning solutions that prioritize performance, generalization, explainability, scalability, and production readiness.
• Apply frameworks such as PyTorch and TensorFlow to build, experiment with, and productionize neural network models.
• Build modular, reusable, observable, secure, and maintainable solutions aligned with enterprise technology patterns.

End-to-End Model and GenAI Delivery

• Own the full lifecycle of AI/ML delivery, including problem framing, data preparation, feature engineering, experimentation, model training, prompt and context design, evaluation, deployment, monitoring, and continuous improvement.
• Develop and productionize NLP and LLM capabilities, including RAG, prompt engineering, model adaptation, fine-tuning where appropriate, and response quality evaluation.
• Train and fine-tune neural network and language models using full fine-tuning, instruction tuning, domain adaptation, and parameter-efficient techniques such as LoRA and QLoRA to optimize performance for specific business tasks and datasets.
• Apply model distillation techniques to compress large teacher models into smaller, efficient student models, balancing accuracy, latency, infrastructure cost, and operational constraints for production deployment.
• Design, train, evaluate, and deploy Small Language Models (SLMs) as lightweight, cost-efficient alternatives to large language models for latency-sensitive, on-device, resource-constrained, or specialized domain use cases, including task-specific SLM fine-tuning and evaluation against larger models.
• Evaluate trade-offs between foundation models, fine-tuned models, distilled models, and Small Language Models to determine the most effective architecture for business, operational, and governance requirements.
• Implement model evaluation frameworks for predictive, generative, and retrieval-based systems, including accuracy, relevance, groundedness, hallucination risk, latency, cost, and robustness.
• Monitor production performance, data drift, model drift, failures, and usage patterns, and drive remediation or optimization.

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