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.
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, BigQuery, 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.
