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Engineering","optional":false},{"name":"Pydantic","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"TGI","optional":false},{"name":"Unstructured.io","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-08-26T05:45:40Z","employer_posted_date":null,"last_verified_at":"2026-08-26T05:45:40Z","board_verified":false,"closed_at":null,"days_open":51,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":51},"description":"Responsibilities:\nAgentic Architecture: Design, deploy, and scale multi-agent orchestration systems and autonomous workflows using cutting-edge frameworks.\nAdvanced RAG Pipelines: Build and optimize advanced retrieval-augmented generation (RAG) pipelines over massive, heterogeneous datasets (structured and unstructured).\nState and Memory Management: Implement robust state management, short/long-term memory systems, and self-correction/reflection loops within agent networks.\nEvaluation and Guardrails: Create and implement robust evaluation metrics, observability pipelines, and guardrails for content quality, hallucination reduction, bias mitigation, and safety standards.\nPerformance Optimization: Monitor and optimize AI inference cost, latency, throughput, token usage, and overall system reliability.\nSecurity and Access Control: Implement robust access controls, data encryption, user authentication, and prompt injection mitigation across all LLM workflows.\nCollaboration and Best Practices: Document and share reusable agent patterns, prompt libraries, and engineering components across cross-functional technical teams.\nRequirements:\nDemonstrated experience taking ownership of ambiguous tasks, successfully driving small to medium initiatives, and acting as a technical mentor.\nProven track record of engaging in knowledge-sharing initiatives, speaking at internal technical events, and navigating group dynamics in diversified settings.\nTechnical Skills:\nGenAI Development: Advanced prompt engineering, fine-tuning, RAG/GraphRAG, schema-constrained outputs, function/tool-calling, and semantic caching.\nAgentic Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, and DSPy.\nVector Databases: FAISS, Milvus, Qdrant, Pinecone, Weaviate, and Pgvector.\nLLM Serving and Infra: vLLM, Hugging Face TGI, and Ollama (for local development).\nGuardrails and Validation: NeMo Guardrails, Guardrails AI, Pydantic, and Instructor.\nLLM Ops and Observability: Ragas, DeepEval, Langfuse, LangSmith, and Phoenix.\nData Parsing: Unstructured, Apache Tika, LlamaParse, and PDFPlumber.\nProgramming and DevOps: Python (FastAPI, asyncio), REST/GraphQL APIs, Git, CI/CD pipelines, Docker, Kubernetes, and OpenTelemetry.\nEmerging Protocols: Model Context Protocol (MCP) and Agent2Agent communication standards.\nAWS Ecosystem (Baseline Experience):\nAmazon Bedrock: Foundation model access, custom configurations, and managed agent workflows.\nAmazon SageMaker: Fine-tuning, hosting, evaluating, and deploying open-source LLMs.\nAmazon OpenSearch Service: Vector search, hybrid search, and enterprise retrieval infrastructure.\nAWS Step Functions: Multi-step orchestration and state machine management for hybrid AI/traditional pipelines.\nAdditional Qualifications:\nProblem-Solving: An analytical mindset capable of breaking down highly abstract, ambiguous logic loops into predictable agent behaviors.\nCollaboration: Ability to thrive in a fast-paced, product-focused agile engineering environment alongside data scientists and product owners.\nCommunication: Strong technical writing and communication skills for documenting complex system architectures and cross-functional collaboration.\nBonus: Domain and Regulatory Knowledge:\nExperience or strong familiarity with the following clinical trial standards will give you a significant advantage:\nClinical Document Automation: Automating the full clinical document generation workflow, specifically translating protocols to Clinical Study Reports (Protocol CSR).\nData Lineage Workflows: Experience transforming raw clinical data and statistical outputs into structured regulatory documents (SDTM/ADaM/ARD TLG CSR).\nCDISC Standards: Deep understanding of CDASH (CRFs), SDTM, ADaM, ARD/ARM, and Define-XML.\nRegulatory Submissions: Familiarity with ICH M11 (protocol/SoA), ICH E3 (CSR), and eCTD Module 5\nCompliance Frameworks: Designing software to align strictly with GxP, 21 CFR Part 11 and ICH guidelines, ensuring total auditability, traceability, and 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