Job Overview
Explore AI Engineer Jobs in Seattle for entry-level, mid-level, and senior professionals looking to build intelligent, scalable, and production-ready AI solutions. This opportunity is ideal for engineers with expertise or strong interest in Artificial Intelligence, Machine Learning, Generative AI, Large Language Models (LLMs), Deep Learning, NLP, Computer Vision, Cloud AI, and MLOps.
AI Engineers will work on the design, development, deployment, and optimization of AI-powered applications and enterprise solutions. Depending on experience level, responsibilities may range from developing machine learning models and building AI APIs to designing enterprise AI architectures, implementing Retrieval-Augmented Generation (RAG) systems, optimizing LLM applications, and leading end-to-end AI initiatives.
Professionals with experience across AWS, Microsoft Azure, Google Cloud, Python, PyTorch, TensorFlow, LangChain, vector databases, Kubernetes, Docker, and modern AI frameworks are encouraged to explore these AI career opportunities in Seattle.
Job Title
Position: AI Engineer / Machine Learning Engineer / Generative AI Engineer
Location: Seattle, Washington, USA
Experience Level: Entry Level to Senior Level
Employment Type: Full-Time / Permanent / Contract Opportunities
Work Model: On-site / Hybrid / Remote, depending on the position
Career Level: Entry-Level, Associate, Mid-Level, Senior & Lead Roles
Job Function: Artificial Intelligence, Machine Learning, Generative AI & Software Engineering
Roles & Responsibilities
- Design, develop, test, and deploy scalable Artificial Intelligence and Machine Learning solutions for real-world business applications.
- Build production-ready machine learning models using structured, unstructured, textual, and multimodal datasets.
- Develop Generative AI applications powered by Large Language Models such as GPT-style, Claude, Gemini, Llama, Mistral, and other foundation models.
- Build enterprise Retrieval-Augmented Generation (RAG) pipelines integrating LLMs with organizational knowledge and data sources.
- Develop AI agents, intelligent assistants, conversational AI platforms, recommendation engines, and automated decision-support applications.
- Implement NLP solutions for text classification, information extraction, semantic search, summarization, document intelligence, and conversational interfaces.
- Develop and optimize deep learning models using PyTorch, TensorFlow, Keras, and Transformer-based architectures.
- Integrate AI capabilities into applications through REST APIs, microservices, SDKs, and cloud-native services.
- Work with vector databases and semantic retrieval technologies including Pinecone, Weaviate, Milvus, FAISS, Chroma, and pgvector.
- Design prompt engineering strategies and evaluate prompts, model responses, retrieval quality, grounding, and overall AI application performance.
- Implement LLMOps and MLOps pipelines covering model training, versioning, testing, deployment, monitoring, observability, and continuous improvement.
- Deploy AI workloads using AWS, Microsoft Azure, or Google Cloud Platform.
- Containerize and orchestrate AI applications using Docker and Kubernetes.
- Optimize model inference for latency, scalability, reliability, infrastructure utilization, and operating cost.
- Establish AI evaluation frameworks to measure model accuracy, relevance, robustness, hallucination risk, and production performance.
- Apply responsible AI practices including model governance, privacy, security, explainability, bias assessment, and data protection.
- Collaborate with data scientists, software engineers, cloud architects, product managers, DevOps teams, and business stakeholders.
- Participate in architecture reviews, code reviews, technical documentation, experimentation, and production troubleshooting.
- Senior engineers may mentor junior team members and lead the technical design of enterprise-scale AI platforms and Generative AI initiatives.
Technical Skill Set
| Skill Area | Technologies / Competencies |
|---|---|
| Programming | Python, SQL, Java, Scala, JavaScript/TypeScript |
| Artificial Intelligence | Machine Learning, Deep Learning, Generative AI, Predictive Modeling |
| LLMs | GPT-style Models, Claude, Gemini, Llama, Mistral, Foundation Models |
| GenAI Frameworks | LangChain, LlamaIndex, Hugging Face, Semantic Kernel |
| RAG | Retrieval-Augmented Generation, Embeddings, Semantic Search, Chunking, Reranking |
| Machine Learning | Scikit-learn, XGBoost, LightGBM, Feature Engineering, Model Evaluation |
| Deep Learning | PyTorch, TensorFlow, Keras, Neural Networks, Transformers |
| NLP | Transformers, BERT, Tokenization, Text Classification, NER, Semantic Search |
| Vector Databases | Pinecone, Weaviate, Milvus, FAISS, Chroma, pgvector |
| Cloud AI | AWS AI/ML, Amazon SageMaker, Azure AI, Azure Machine Learning, Google Vertex AI |
| MLOps / LLMOps | MLflow, Kubeflow, Model Registry, Model Monitoring, CI/CD |
| DevOps | Docker, Kubernetes, Git, GitHub Actions, Jenkins, Terraform |
| Data Engineering | Spark, Kafka, ETL/ELT, Data Pipelines, Data Lakes |
| APIs | REST API, FastAPI, Flask, Microservices, API Integration |
| Databases | PostgreSQL, MySQL, MongoDB, Redis, NoSQL |
| AI Engineering | Prompt Engineering, Fine-Tuning, Model Evaluation, AI Agents, Function Calling |
| Security & Governance | Responsible AI, AI Security, Model Governance, Privacy, Access Control |
| Architecture | Distributed Systems, Cloud-Native Architecture, Scalable AI Systems |
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, or a related technical discipline.
- Strong programming knowledge, preferably using Python.
- Understanding of machine learning concepts including supervised and unsupervised learning, model training, validation, feature engineering, and evaluation.
- Familiarity with data structures, algorithms, statistics, probability, and software engineering fundamentals.
- Hands-on experience with at least one ML or deep learning framework such as PyTorch, TensorFlow, or Scikit-learn.
- Understanding of REST APIs, databases, Git, and modern software development practices.
- Ability to analyze datasets and translate business requirements into practical AI solutions.
- Entry-level applicants should demonstrate relevant academic projects, internships, research, certifications, or hands-on AI projects.
- Experienced candidates should demonstrate successful development or deployment of AI/ML applications in production environments.
- Senior candidates should have experience designing scalable AI systems and making architecture-level technical decisions.
Preferred Qualifications
- Experience developing production-grade Generative AI and LLM applications.
- Hands-on knowledge of RAG architecture, embeddings, vector search, reranking, and prompt engineering.
- Experience with LangChain, LlamaIndex, Hugging Face, or comparable AI development frameworks.
- Knowledge of fine-tuning, parameter-efficient tuning, model optimization, and inference techniques.
- Experience deploying AI/ML workloads on AWS, Azure, or Google Cloud.
- Familiarity with Amazon SageMaker, Azure AI, Azure Machine Learning, or Vertex AI.
- Experience with Docker, Kubernetes, Terraform, CI/CD, MLflow, or Kubeflow.
- Understanding of AI agents, tool calling, multimodal AI, and enterprise AI integration patterns.
- Experience with model monitoring, observability, AI evaluation, and production troubleshooting.
- Familiarity with responsible AI, privacy, model governance, and AI security principles.
- Contributions to open-source AI/ML projects, technical publications, patents, or research are advantageous for specialized and senior positions.
Desired Soft Skills
- Strong analytical thinking and structured problem-solving ability.
- Ability to translate complex AI concepts into practical business solutions.
- Excellent verbal and written communication skills.
- Strong collaboration skills across engineering, product, data, and business teams.
- Curiosity and willingness to continuously learn emerging AI technologies.
- Ability to experiment rapidly while maintaining production-quality engineering standards.
- Strong ownership and accountability for assigned AI solutions.
- Attention to detail when evaluating model quality, data integrity, and system performance.
- Ability to work effectively in fast-paced and evolving technology environments.
- Leadership, mentoring, architecture decision-making, and stakeholder-management capabilities for senior-level positions.
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