Platform Engineer (Semantic Discovery & Credentials)

We are looking for a Platform Engineer to build secure discovery and credential management capabilities for enterprise AI platforms. The role focuses on semantic search, vector-based discovery, secrets management, credential access services, and AI tool classification. The ideal candidate has hands-on experience with Python backend development, secrets management solutions, and modern semantic search technologies.

Our customer is a multinational corporation with more than a century of history and offices in over 180 countries. Their most ambitious goal at the time is to introduce a range of Reduced-Risk Products (RRPs). The target audience is more than 1 billion consumers around the globe. IT platform hosts 700+ applications.

Intellia's mission is to help the client with the engineering of a comprehensive software ecosystem for a game-changing IoT product on the margin of innovative consumer experience and cutting-edge technology. Our teams are involved in the engineering of core platform components for best-in-class eCommerce, Digital Marketing and IoT solutions. As an Engineer, you will become a part of Core Architecture Team and be responsible for the architecture, implementation of best practices in our Digital Engineering Enterprise Platform.

The Platform is a set of services and internet applications that accelerate the development and delivery of software applications by taking care of common SDLC challenges. The Platform provides access and consumption for engineering teams to a set of services, technologies, practices for their development and for operating their application, ensuring a set of compliance and best practices.

Requirements:

• Semantic discovery implementation (vector embeddings, OpenSearch or pgvector)
• Vault (HashiCorp) integration for credential management
• AWS Secrets Manager as an alternative
• Python credential retrieval API
• MCP server capability and semantic domain taxonomy

Experience:

• 4+ years platform or backend engineering
• Secrets management (Vault or AWS Secrets Manager)
• Vector search or semantic search implementation

Will be a plus:

• AWS Bedrock embedding models for semantic indexing
• OpenSearch Service or pgvector on RDS
• OAuth token lifecycle management

Responsibilities:

• Design and implement semantic discovery capabilities for MCP servers, tools, and agent ecosystems.
• Build and maintain vector-based search solutions using embeddings, OpenSearch, pgvector, or similar technologies.
• Develop metadata taxonomies and capability classification models for MCP services and AI tools.
• Design and implement secure credential retrieval services using Python and cloud-native architectures.
• Integrate HashiCorp Vault and AWS Secrets Manager to support secure secret storage and access management.
• Build APIs for credential retrieval, access validation, and secure secret consumption.
• Implement governance controls for credentials, service identities, and tool access policies.
• Design semantic indexing pipelines and relevance-ranking strategies for discovery services.
• Collaborate with platform, security, and AI engineering teams to establish discovery and credential management standards.
• Implement observability, auditability, and monitoring for secret access and discovery workflows.
• Support lifecycle management of credentials, service identities, and access tokens across distributed systems.
• Produce technical documentation, architecture guidelines, and operational playbooks.