Explore AI roles

Keyboard friendly • Copy, share & save roles

Sort by

As an AI Engineer , you will design, build, and operate agentic AI systems end-to-end —from concept to production. Yo...

Fulltime Ai Ai Engineer Machine Learning

Fully remote

Apply

Accurate Job Title: AI ML Engineer Location – San Jose, CA Hybrid (3 days WFO) Top 3 Skills Candidates Should Possess...

Contract Ai Machine Learning Data Science

Fully remote

Apply

Gen AI Developer Location : Washington, DC Duration : Fulltiime Onsite Must Have: Large Language Models (LLMs), Retri...

Ai Mlops Generative Ai Data Engineer

Fully remote

Apply

Selected role

AI Engineer – Agentic RAG Systems

Smart IT Frame LLC

Live

Full-time Posted 16 days ago 3 min read

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.

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 >

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.

Ready to apply? You'll go straight to the hiring team.

No agency in the middle · Your profile is shared privately.

Explore the complete AI ecosystem

Discover the best AI-first companies, podcasts, events, learning paths, and jobs in one focused hub.

AI Learning Hub · Always curated, never noisy

Simple pricing for serious AI hiring

Pay per listing. No subscriptions, no hidden fees. Designed for teams hiring for high-impact AI roles.

No-risk: replace or refund if the role doesn’t get traction.

Standard listing

Reach active AI talent

For startups

$349 per role

30-day live listing in the AI job feed.
Structured posting written for AI engineers (we’ll help refine copy).
Instant alerts to subscribed candidates for your tags (LLMs, agents, infra, etc.).

Most teams see first applicants within 24–48 hours.

Max visibility to senior talent

For scale-ups

$649 per role

Everything in Standard, plus homepage & “Featured role” placement.
Priority placement in weekly AI roles newsletter.
1:1 review of your role with DevFound to calibrate for top-tier candidates.
Highlight badge on job page to increase conversion.

Best for teams hiring 1–3 critical AI roles this quarter.

Smart IT Frame LLC Jobs

Explore AI roles

AI Engineer – Agentic RAG Systems

AI ML Engineer

Generative AI Engineer

Explore the complete AI ecosystem

Choose Your Plan

Simple pricing for serious AI hiring

Reach active AI talent

Max visibility to senior talent