Sr AI Platform Engineer Job Dearborn area,Michigan USA,Software Development

Sr AI Platform Engineer

The Senior AI Platform Engineer is responsible for designing, developing, and deploying intelligent AI-powered applications, multi-agent systems, and automation solutions that leverage Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and cloud-native technologies. This role combines advanced software engineering, cloud architecture, and AI application development to build scalable, secure, observable, and production-ready solutions that transform data into actionable business insights.

The ideal candidate has experience building and deploying enterprise-grade AI applications and agent-based architectures, with expertise in Google Cloud Platform (GCP), Python development, cloud data technologies, and modern AI frameworks.

Key Responsibilities

Design, architect, and deploy production-grade multi-agent AI systems using modern orchestration frameworks and state management capabilities.
Develop intelligent applications, cognitive services, and AI-powered workflows that automate processes, generate recommendations, identify patterns, predict outcomes, and enable self-service capabilities.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings, hybrid retrieval, reranking, and retrieval evaluation.
Develop and maintain secure integrations between AI applications, enterprise data platforms, and operational systems.
Design, implement, and optimize AI models and algorithms to solve complex business and operational challenges.
Build and maintain cloud-native AI services on Google Cloud Platform, including Cloud Run, GKE, Vertex AI, Big Query, and Pub/Sub.
Establish CI/CD pipelines, containerized deployments, infrastructure automation, and software delivery best practices.
Implement observability, monitoring, evaluation frameworks, and tracing capabilities for AI and agent-based systems.
Develop guardrails, validation mechanisms, prompt security controls, and human-in-the-loop workflows to ensure safe and reliable AI operations.
Collaborate with data scientists, software engineers, product teams, and business stakeholders to operationalize AI solutions.
Drive performance, scalability, reliability, and cost optimization strategies across AI platforms and services.
Research emerging AI technologies and evaluate opportunities to enhance organizational capabilities and business outcomes.

Required Skills

Google Cloud Platform (GCP)
Python
Large Language Models (LLMs)
Generative AI
Agentic AI Frameworks
Retrieval-Augmented Generation (RAG)
SQL
REST APIs
Docker
Kubernetes
CI/CD Pipelines
Git Hub Actions
Cloud Data Platforms

Required Experience

Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field.
3+ years of experience developing and deploying production software systems.
1-2+ years of experience building and supporting AI, machine learning, generative AI, or LLM-powered applications.
Experience designing and deploying multi-agent, distributed, or service-oriented architectures in production environments.
Strong Python programming skills, including asynchronous and concurrent application development.
Experience with backend frameworks such as FastAPI or Flask.
Hands-on experience with agent orchestration frameworks such as Lang Graph, CrewAI, Llama Index, or similar technologies.
Experience building and optimizing RAG solutions, including vector databases, embeddings, chunking strategies, and retrieval evaluation.
Experience with vector databases such as Pinecone, Weaviate, Qdrant, or pgvector.
Experience deploying solutions to cloud platforms, preferably Google Cloud Platform.
Strong SQL and cloud data warehouse experience.
Experience with containerization and cloud-native deployment technologies, including Docker and Kubernetes.
Experience building evaluation, monitoring, and observability frameworks for AI applications.
Understanding of AI safety principles, prompt injection protection, output validation, and secure execution practices.
Strong software engineering fundamentals, including testing, API design, version control, scalability, security, and…