AI Engineer – Agentic & RAG Systems
Full Time • Fully Remote - US
Job Title: AI Engineer – Agentic & RAG Systems
Location: Remote
Department: AI & Data Platforms
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-end—from concept to production. You’ll 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 teams—bringing ideas to life through strong engineering execution, clear communication, and a low-ego, problem-solving mindset.
You will 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.
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 accuracy—using 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 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.
- 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.
Compensation: $130,000.00 per year
About
In the perfect storm of technology forces shaping the business world, the question is not whether companies need to adapt to stay competitive but which tech partner to trust for long-term business improvement and transformation needs.
That’s why iShare was founded: to be that credible IT partner that cuts through the buzzword clutter to deliver real value by real people. And that starts with doing the basics right.
Who We Are
Based in New Jersey, iShare is a boutique IT consulting firm with strategic advisory, development, and staffing services designed for specific industry verticals. iShare was founded to fill a real market need – with so much noise about cloud, big data, and information security, companies today are adopting software product after product and losing sight of which they need and why, while what they really need is credible consulting that takes the time to understand their company’s unique scenario and requirement, and then architect a solution accordingly.
Recognizing this gap, senior industry executives put their credibility on the line to form iShare and ensure they create a fulfilling experience with lasting value for all stakeholders – customers, employees and partners. iShare leadership team has 20+ years average IT experience and is well-rounded in technology, operations, account and customer management. Our experience spans C-Suite positions with Fortune 100 firms as well as mid-tier firms. Our background includes stints at multibillion-dollar ITO / BPO firms as well as startups.
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