<p><strong>AI Lead Engineer</strong></p><p><br></p><p><strong>Skills:</strong></p><p>• Proactive self-starter with excellent interpersonal, communication, and customer service skills.</p><p>• Expert-level AI/ML and full-stack development skills, with strong hands-on experience building and integrating backend services and frontend applications using modern frameworks such as Node.js and React. Strong emphasis on clean, maintainable, reproducible, well-tested, and well-documented code.</p><p>• Ability to manage multiple tasks and projects simultaneously.</p><p>• Collaborative team player with a focus on achieving common goals.</p><p>• Meticulous attention to detail.</p><p>• Quick learner with a passion for staying current with emerging technologies and industry trends.</p>

<p><strong>Required Experience:</strong></p><p>• Deep expertise in RAG systems, LLMs, embeddings, vector databases, and AI infrastructure</p><p>• Experience designing semantic retrieval and knowledge platforms, including curated corpora and grounding/citation patterns (e.g., “show your sources” for internal auditability)</p><p>• Experience evaluating AI models for different tasks</p><p>• Strong ability and experience to leverage cloud infrastructure</p><p>• Experience with data quality and metadata management (data lineage, dataset versioning, business glossary/taxonomy) and implementing automated quality checks and anomaly detection</p><p>• Experience implementing secure GenAI platform controls, including prompt logging, red-teaming, content filtering/leakage prevention, and model access controls/tenant isolation</p><p>• Excellent collaboration skills and ability to work with non-technical stakeholders</p><p>• Proven ability to work in existing codebases, improve reliability, performance, and readability over time.</p><p>• 5+ years of hands-on experience in machine learning engineering, with significant work developing and maintaining AI systems in production.</p><p>• Strong software engineering practices including modular design, refactoring, and technical debt management; unit, integration, and regression testing; as well as code reviews and shared coding standards</p><p>• Strong expertise in machine learning fundamentals and statistical modeling, including, but not limited to: </p><p>o Supervised, unsupervised, and reinforcement learning </p><p>o Model evaluation, bias, overfitting, and error analysis </p><p>o Probabilistic and statistical reasoning </p><p>• Proficiency in Python and major ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, HuggingeFace, LangChain, LlamaIndex).</p><p>• Hands-on experience with cloud-based ML platforms (AWS SageMaker, Azure ML, or Google AI Platform).</p>

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