AI Engineer – Agentic, RAG Systems

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November 20

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iShare Inc.

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B2B • Enterprise

iShare Inc. is a New Jersey–based boutique IT consulting firm that provides strategic advisory, software development, and staffing services focused on helping organizations adopt the right technologies for business improvement and transformation. The firm emphasizes practical, people-centered consulting—guided architecture, automation, BI, and information security—delivered by senior leaders with deep enterprise experience across industries such as chemicals/manufacturing, healthcare, software, and professional services. iShare positions itself as a trusted, hands-on B2B partner offering white‑glove service and industry-aware IT solutions to mid-market and enterprise clients across the US.

11 - 50 employees

Founded 2017

🤝 B2B

🏢 Enterprise

📋 Description

• 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.

🎯 Requirements

• 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. • 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.