AI Engineering Talent Profiles
Vetted AI professionals matched to your team's needs — from individual contributors to technical leadership.
Multi-Agent Systems Engineers
Engineers who build systems where multiple AI models work together — handling orchestration, routing, tool use, and state management in production environments.
Request This Profile ›Key Skills & Experience
- Agent orchestration frameworks (LangGraph, LangChain)
- LLM routing and task delegation
- Tool-use design and conflict resolution
- Fault-tolerant pipeline architecture
- Agent memory and state management
- Human-in-the-loop workflows
RAG Engineers
Specialists who build retrieval-augmented generation systems — connecting AI models to your data with accuracy and scale.
Request This Profile ›Key Skills & Experience
- Document processing and chunking strategies
- Vector databases and hybrid search
- Reranking and relevance optimization
- Caching strategies for cost reduction
- Multi-modal document handling
- Retrieval quality evaluation and metrics
LLMOps Engineers
Professionals who bring production discipline to LLM-powered systems — tracing, evaluation, monitoring, and optimization.
Request This Profile ›Key Skills & Experience
- Observability and tracing (Langfuse, LangSmith)
- Prompt management and versioning
- Guardrails and content safety
- Multi-model fallback strategies
- Cost tracking and token optimization
- Evaluation pipeline design
AI Platform Engineers
Engineers who build the infrastructure and tooling that makes AI systems reliable, safe, and easy to develop against.
Request This Profile ›Key Skills & Experience
- Sandboxed execution environments
- Security scanning and safety pipelines
- Automated testing for AI systems
- CI/CD for ML and LLM workloads
- Developer tooling and SDKs
- Platform reliability and SLAs
Cloud AI Engineers
Professionals who design and manage cloud infrastructure optimized for AI workloads — scalable, secure, and cost-efficient.
Request This Profile ›Key Skills & Experience
- AWS (Bedrock, SageMaker, EKS, Lambda)
- Azure (OpenAI Service, AKS, Cognitive Services)
- Kubernetes orchestration for AI
- CI/CD and infrastructure as code
- Auto-scaling inference endpoints
- Cost optimization and capacity planning
AI Architects & Tech Leads
Senior engineers who lead AI initiatives — from system design and vendor evaluation to team building and production rollout.
Request This Profile ›Key Skills & Experience
- End-to-end AI system architecture
- Technology evaluation and selection
- AI readiness and feasibility assessments
- Team mentorship and upskilling
- Proof-of-concept to production planning
- Build vs buy analysis
Our Approach
What makes working with RNJ different
Technical Vetting by Practitioners
Our assessments are designed by AI engineers, not recruiters. Candidates go through system design discussions, hands-on coding, and domain-specific evaluations before we present them to you.
AI-Focused Specialization
We don't staff generalist developers. Every candidate in our network specializes in AI engineering — from RAG and multi-agent systems to LLMOps and cloud AI infrastructure.
Embedded Team Integration
Our engineers don't work in silos. They join your standups, submit PRs to your repos, follow your processes, and operate in your timezone. They're your team members, not outsourced contractors.
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