AI Engineer

• Design, build, and operate agentic AI systems end-to-end—from concept to production.
• Work on multi-agent orchestration, Retrieval-Augmented Generation (RAG), evaluation frameworks, and AI guardrails to build safe, reliable, and high-performing systems.
• Collaborate cross-functionally with product, ML, and design teams—bringing ideas to life through strong engineering execution, clear communication, and a low-ego, problem-solving mindset.
• Design and implement Retrieval-Augmented Generation pipelines to ground LLMs in enterprise or domain-specific data.
• Make strategic decisions on chunking strategy, embedding models, and retrieval mechanisms to balance context precision, recall, and latency.
• Work with vector databases (Qdrant, Weaviate, pgvector, Pinecone) and embedding frameworks (OpenAI, Hugging Face, Instructor, etc.).
• Diagnose and iterate on challenges like chunk size trade-offs, retrieval quality, context window limits, and grounding accuracy—using structured evaluation and metrics.
• Establish comprehensive evaluation frameworks for LLM applications, combining quantitative (BLEU, ROUGE, response time) and qualitative methods (human evaluation, LLM-as-a-judge, relevance, coherence, user satisfaction).
• Implement continuous monitoring and automated regression testing using tools like LangSmith, LangFuse, Arize, or custom evaluation harnesses.
• Identify and prevent quality degradation, hallucinations, or factual inconsistencies before production release.
• Collaborate with design and product to define success metrics and user feedback loops for ongoing improvement.
• Implement multi-layered guardrails across input validation, output filtering, prompt engineering, re-ranking, and abstention (“I don’t know”) strategies.
• Use frameworks such as Guardrails AI, NeMo Guardrails, or Llama Guard to ensure compliance, safety, and brand integrity.
• Build policy-driven safety systems for handling sensitive data, user content, and edge cases with clear escalation paths.
• Design and operate multi-agent workflows using orchestration frameworks such as LangGraph, AutoGen, CrewAI, or Haystack.
• Coordinate routing logic, task delegation, and parallel vs. sequential agent execution to handle complex reasoning or multi-step tasks.
• Build observability and debugging tools for tracking agent interactions, performance, and cost optimization.
• Evaluate trade-offs around latency, reliability, and scalability in production-grade multi-agent environments.

AI Engineer – Agentic, RAG Systems

Senior AI Engineer

AI Engineer Intern, Summer 2026

Product Manager – Conversational AI Platform

Senior AI Engineer, AI Services

AI Engineer, AI Services