AI Platform Engineer Business Applications
Skills
About the Role
You will build and own the AI data foundation for scalable AI and GenAI use cases. You will design and implement RAG pipelines, optimize embedding and vector-indexing strategies, and build batch and real-time data ingestion pipelines for structured and unstructured sources. You will develop scalable Python services and APIs, integrate enterprise systems, enforce data-governance controls, and operate cloud-native services with logging, monitoring, and AI observability. You will collaborate across functions to maintain quality throughout the software development lifecycle.
Requirements
- 3 to 5 years of software AI development experience
- Experience with AI development practices and design patterns
- Expertise using Python, FastAPI, PyTest, Celery, and other Python frameworks
- Familiarity with AI/ML, GenAI, and MLOps concepts
- Hands-on experience with object-oriented programming, concurrency, design patterns, and REST APIs
- Hands-on experience with RAG and vector databases
- Familiarity with Kubernetes, Terraform, and GitHub Actions
- Working knowledge of logging, monitoring, and AI observability practices
- Experience with AWS, Microsoft Azure, or GCP
- Bachelor's degree in engineering or equivalent
Responsibilities
- Design and implement RAG pipelines, including retrieval, ranking, and context injection
- Optimize embedding strategies and vector indexing
- Build real-time and batch data ingestion pipelines for structured and unstructured sources
- Develop scalable services using Python, APIs, and microservices architecture
- Integrate services with enterprise systems, including CRM, knowledge bases, and data lakes
- Enforce data access policies, mask sensitive information, and ensure compliance standards
- Ensure traceability and auditability of AI outputs
- Deploy and operate cloud-native services with logging, monitoring, and AI observability
- Work with cross-functional teams to ensure quality throughout the software development lifecycle
- Improve retrieval accuracy and response quality of AI-driven systems
- Build reusable, modular platform components that promote standardization