AI Engineer Agentic & RAG Systems
Job Title: AI Engineer Agentic & RAG Systems
Location: Remote
Department: AI & Data Platforms
Only US Citizens or Green Card Holders
About the Role
As an AI Engineer, you will design, build, and operate agentic AI systems end-to-endfrom concept to production. Youll work on multi-agent orchestration, Retrieval-Augmented Generation (RAG), evaluation frameworks, and AI guardrails to build safe, reliable, and high-performing systems.
You will collaborate cross-functionally with product, ML, and design teamsbringing ideas to life through strong engineering execution, clear communication, and a low-ego, problem-solving mindset.
Key Responsibilities
1. RAG Development & Optimization
• 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 accuracyusing structured evaluation and metrics.
2. Chatbot Quality & Evaluation Frameworks
• 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.
3. Guardrails, Safety & Responsible AI
• Implement multi-layered guardrails across input validation, output filtering, prompt engineering, re-ranking, and abstention (I dont 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.
• Balance safety, user experience, and helpfulness, knowing when to block, rephrase, or gracefully decline responses.
4. Multi-Agent Systems & Orchestration
• 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.
Minimum Qualifications
• Strong proficiency in Python (FastAPI, Flask, asyncio) and GCP experience is good to have
• Demonstrated hands-on RAG implementation experience with specific tools, models, and evaluation metrics.
• Practical knowledge of agentic frameworks (LangGraph, LangChain) and evaluation ecosystems (LangFuse, LangSmith).
• Excellent communication skills, proven ability to collaborate cross-functionally, and a low-ego, ownership-driven work style.
Preferred / Good-to-Have Qualifications
• Experience in traditional AI/ML workflows e.g., model training, feature engineering, and deployment of ML models (scikit-learn, TensorFlow, PyTorch).
• Familiarity with retrieval optimization, prompt tuning, and tool-use evaluation.
• Background in observability and performance profiling for large-scale AI systems.
• Understanding of security and privacy principles for AI systems (PII redaction, authentication/authorization, RBAC)
• Exposure to enterprise chatbot systems, LLMOps pipelines, and continuous model evaluation in production.
This is a remote position.
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