Sr AI Platform Engineer

Description: 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.

Job#: 3047770

Job Description:

Role: Sr AI Platform Engineer

Location: Hybrid

Duration: Long-Term Contract

Rate: Negotiable based on experience

Description: 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.

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 coding best practices.

Preferred Experience

• Experience implementing model routing, cost optimization strategies, and AI workload management at scale.
• Experience deploying human-in-the-loop validation frameworks and high-reliability AI solutions.
• Experience supporting automotive, EV charging, IoT, telematics, or connected vehicle ecosystems.
• Familiarity with Model Context…