Expert Enterprise AI Engineer – AI Platform / Solution Lead

Expert Enterprise AI Engineer – AI Platform / Solution Lead
Location: Remote, but Oakland, CA, Rancho Cordova, CA or Alpharetta, GA locations are preferred for occasional in-person activities
Duration: 6+ months CTH

Job Summary
We are seeking an Expert Enterprise AI Engineer to help define, architect, build, and implement foundational AI platform capabilities while remaining hands-on in the design and delivery of enterprise AI solutions. This role combines AI platform engineering, solution architecture, machine learning, generative AI, and end-to-end technical delivery.
The ideal candidate will have strong experience building scalable AI/ML platforms and production-grade AI solutions, with the ability to translate business requirements into robust technical architectures. The role will work closely with data scientists, ML engineers, software engineers, cloud/platform teams, product leaders, and business stakeholders to establish reusable AI capabilities and accelerate enterprise AI adoption.

Key Responsibilities

AI Platform & Architecture

• Define and implement foundational capabilities for an enterprise AI/ML platform, including reusable services, frameworks, APIs, tooling, and deployment patterns.
• Design scalable, secure, reliable, and cost-effective architectures for AI and ML workloads.
• Establish standards and best practices for AI development, model deployment, monitoring, governance, security, and lifecycle management.
• Evaluate and integrate AI/ML technologies, frameworks, cloud services, and third-party platforms.
• Develop reusable components and patterns that enable teams to rapidly build and deploy AI solutions.
• Partner with cloud and infrastructure teams to establish AI-ready environments across AWS, Azure, GCP, or hybrid cloud environments.

Hands-on AI Solution Delivery

• Lead the design and hands-on implementation of enterprise AI and machine learning solutions.
• Develop and productionize machine learning, deep learning, Generative AI, LLM, NLP, and AI-agent solutions where appropriate.
• Design solutions using LLMs, RAG, vector databases, embeddings, prompt engineering, model orchestration, and AI agents.
• Build proof-of-concepts and rapidly convert successful prototypes into scalable production solutions.
• Develop APIs, services, pipelines, and integrations required to operationalize AI capabilities.
• Work with engineering teams to integrate AI solutions into existing enterprise applications and business processes.

MLOps / LLMOps

• Establish and implement MLOps/LLMOps practices for model training, validation, deployment, monitoring, and continuous improvement.
• Build automated CI/CD pipelines for AI/ML applications and models.
• Implement model performance, quality, drift, latency, cost, and reliability monitoring.
• Establish processes for model versioning, experimentation, evaluation, and lifecycle management.
• Develop automated evaluation frameworks for Generative AI and LLM-based applications.

Enterprise AI Governance & Security

• Help establish responsible AI practices covering security, privacy, governance, compliance, explainability, and responsible use of AI.
• Implement appropriate controls for data protection, model access, prompt security, and AI application security.
• Collaborate with enterprise architecture, cybersecurity, data governance, and risk teams.
• Help define standards for selecting, developing, deploying, and managing enterprise AI solutions.

Technical Leadership

• Serve as a technical subject matter expert for enterprise AI initiatives.
• Provide technical guidance and mentorship to AI/ML engineers, developers, and data scientists.
• Collaborate with product and business stakeholders to translate business problems into AI-enabled solutions.
• Drive technical decisions around architecture, technology selection, scalability, and integration.
• Communicate complex AI concepts and architectural decisions effectively to both technical and non-technical stakeholders.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.
• 8+ years of experience in software engineering, data engineering, machine learning, AI engineering, or related technical disciplines.
• Strong hands-on experience designing and delivering enterprise AI/ML solutions.
• Experience architecting or implementing AI/ML platforms and reusable AI capabilities.
• Strong programming experience with Python and familiarity with APIs, microservices, and distributed systems.
• Experience with one or more major cloud platforms: AWS, Azure, or GCP.
• Strong understanding of machine learning lifecycle, MLOps, model deployment, monitoring, and productionization.
• Hands-on experience with Generative AI and LLM technologies.
• Experience with RAG, vector databases, embeddings, prompt engineering, LLM evaluation, and AI agents.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Strong understanding of data pipelines, databases, APIs, and enterprise application integration.
• Strong architecture, problem-solving, and technical communication skills.

Preferred Qualifications

• Experience building enterprise AI platforms or AI Centers of Excellence.
• Experience with frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar AI orchestration frameworks.
• Experience with ML platforms such as Databricks, SageMaker, Azure ML, Vertex AI, or similar technologies.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, pgvector, or OpenSearch.
• Experience with AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
• Experience with agentic AI, multi-agent systems, tool calling, and workflow orchestration.
• Knowledge of AI security, responsible AI, model governance, and enterprise compliance.
• Experience integrating AI solutions with enterprise systems, data platforms, and APIs.