Principal AI/ML Engineer with 15+ Years of experience MUST Needed

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Role: Principal AI/ML Engineer

Location: Seattle, WA (Hybrid)

Duration: Long Term

Job Summary

Overall, 12-15 Years with 5-6 years experience in AI/ML

Must have skill

Python/Java, TensorFlow / PyTorch, LangChain, Hugging Face.

Hands-on with LLMs, embeddings, fine-tuning, and vector databases (FAISS, Pinecone, Chroma, etc.).

Familiarity with NLP, text summarization, classification, semantic search, and RAG pipelines.

Understanding of data engineering pipelines.

Role Overview

We are seeking a seasoned Principal AI/ML Engineer to lead the design, development, and deployment of cutting-edge AI/ML solutions. This role requires a visionary leader who can define technical strategy, mentor teams, and drive innovation in Generative AI, LLMs, and advanced machine learning techniques.

Key Responsibilities -

• Architect AI/ML Solutions: Define and implement scalable AI/ML architecture, frameworks, and standards.
• End-to-End Model Lifecycle: Oversee data preparation, model training, deployment, and performance monitoring.
• MLOps Excellence: Build robust ML Ops pipelines and reusable components for production-grade systems.
• Leadership & Mentorship: Guide senior engineers and cross-functional teams to deliver impactful AI solutions.
• Innovation in Generative AI: Drive adoption of LLMs, Retrieval-Augmented Generation (RAG), and advanced ML methodologies.
• Governance & Compliance: Ensure adherence to AI governance, fairness, security, and ethical principles.
• Strategic Alignment: Collaborate with business and product leaders to align AI initiatives with organizational goals. Required Skills & Experience
• Technical Expertise:
• Strong proficiency in Python, TensorFlow/PyTorch, Scikit-learn, and Transformers.
• Hands-on experience with LLM fine-tuning, RAG, and vector databases (FAISS, Pinecone).
• MLOps & Cloud Platforms: o Proficient in MLflow, Airflow, Docker, Kubernetes.
• Experience with AWS SageMaker, Google Cloud Platform Vertex AI, or Azure ML.
• Data & Governance: o Deep understanding of data engineering, model governance, and AI ethics.
• Leadership Skills: o Excellent communication, stakeholder management, and ability to influence at all levels.