Staff Engineer - AI Platform

• Build new features, enhance existing ones, and support them in production, focusing on the AI platform.

• Build reusable libraries or technology platforms that address multiple use cases.

• Work closely with Engineers to develop the best technical design, strategy, and drive execution to build capabilities into the platform.

• Own service for assigned services, including functional availability, correctness, and security.

• Own the delivery of various timelines, ensuring that key milestones are met and deliveries are of the highest quality.

• Establish and encourage the adoption of software development best practices across the team and the organization.

• Collaborate with non-technical stakeholders such as Product Managers, Designers, and Marketing.

• Encourage and mentor talented engineers, working with them to remove any roadblocks.

• Deploy and maintain enterprise-class RESTful web services.

• Own the engineering excellence and operational readiness of the service, driving the SLO, SLI, and SLA of the relevant services.

• Take ownership to drive quality via integration and unit test coverage.

• Dive deep into each issue, own reactive fixes, and execute long-term fixes, assisting other Support Engineers on complex RCA issues.

• Provide technical mentoring and L3 engineering support to other engineers.

Core AI & ML Skills

• AI System Design

  • Design and implement AI workflows and agent architectures using frameworks like LangGraph.

  • Build production-grade RAG systems with appropriate chunking, retrieval, and response generation.

  • Design conversation management with context handling, session state, and error recovery.

  • Architect customer-facing AI features with proper validation, fallbacks, and graceful degradation.

  • Implement tool orchestration patterns for connecting LLMs to existing APIs and services.

• LLM Integration

  • Production implementation of LLM APIs with retry logic, fallbacks, and rate limit handling.

  • Practical prompt engineering: system prompts, few-shot learning, structured outputs (JSON mode).

  • Implement evaluation approaches for AI output quality (automated checks, regression testing).

  • Optimize token usage and manage API costs effectively.

  • Awareness of prompt versioning and systematic iteration practices.

• Data Pipelines for AI

  • Build data ingestion pipelines for AI systems (document processing, embedding generation).

  • Implement vector storage and retrieval workflows for RAG and search use cases.

  • Design feedback loops from production AI usage back to improvement cycles.

  • Basic data quality practices for AI inputs (validation, cleaning, deduplication).

• AI Safety & Quality

  • Implement input validation and output filtering for production AI systems.

  • Build or integrate content safety layers (keyword filters, classifier-based detection).

  • Design guardrails that balance safety with usability (avoiding over-blocking).

  • Implement logging and auditing for AI interactions to support compliance and debugging.

Core Backend & Platform Engineering

• Backend Engineering

  • Strong proficiency in Go, Node.js, or Java microservices.

  • Design and optimize high-throughput APIs for production workloads.<