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AI Engineer Agentic & RAG Systems

hace 13 días

New York

Job DescriptionJob Title: AI Engineer Agentic & RAG Systems Location: Remote Department: AI & Data Platforms 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., • 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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