Principal Machine Learning Engineer Agentic AI Platforms
2 days onsite in Raleigh, NC
Long Term
Position Summary
We are seeking a Principal Machine Learning Engineer with 10+ years of experience building large-scale distributed machine learning systems and modern AI platforms. This role will lead the architecture and implementation of Agentic AI ecosystems, LLM infrastructure, RAG platforms, model-serving systems, and AI engineering frameworks supporting enterprise-wide AI adoption.
This is a deeply hands-on role requiring expertise in platform architecture, ML systems design, distributed computing, MLOps, LLMOps, and production AI deployment.
Key Responsibilities
Agentic AI Platform Engineering
- Design and implement enterprise-grade agent frameworks supporting:
- Multi-agent collaboration
- Planner-executor architectures
- Tool-use workflows
- Memory systems
- Human-review workflows
- Build orchestration services and runtime environments for AI agents.
- Implement MCP and agent interoperability standards.
- Design state-sharing and context management systems.
LLM Platform Development
- Build scalable LLM platforms supporting:
- OpenAI
- Claude
- Gemini
- Llama
- Mistral
- Fine-tuned models
- Implement model routing and workload balancing across models.
- Design low-latency AI serving architectures.
RAG & Retrieval Engineering
- Build retrieval systems using:
- Hybrid search
- Dense retrieval
- BM25
- Re-ranking pipelines
- Graph-based retrieval
- Optimize vector stores and embedding pipelines.
- Design retrieval infrastructure capable of supporting billions of documents.
MLOps & LLMOps
- Build automated pipelines for:
- Training
- Evaluation
- Deployment
- Monitoring
- Retraining
- Implement CI/CD pipelines for AI systems.
- Build observability frameworks including:
- OpenTelemetry
- LangSmith
- LangFuse
- Custom tracing
Reliability & Evaluation
- Implement release gates and quality controls.
- Develop evaluation pipelines for:
- Hallucination detection
- Retrieval quality
- Agent performance
- Cost optimization
- Latency management
- Design monitoring frameworks for drift, degradation, and reliability.
Technical Leadership
- Lead architecture reviews for AI systems.
- Establish engineering standards and best practices.
- Mentor engineering teams.
- Partner with Data Scientists, Product Managers, and Business Stakeholders.
Required Qualifications
- Bachelor's, Master's, or PhD in Computer Science, Engineering, Data Science, or related field.
- 10+ years of software engineering or machine learning engineering experience.
- 5+ years building production ML systems.
- 3+ years building production GenAI platforms.
- Strong expertise in:
- Python
- SQL
- Spark
- Distributed computing
- Kubernetes
- Docker
Experience with:
- LangGraph
- LangChain
- LlamaIndex
- Vector databases
- FastAPI
- REST APIs
- Cloud platforms (AWS, Azure, Google Cloud Platform)
Preferred Qualifications
- Experience building multi-agent platforms at enterprise scale.
- Experience implementing MCP and A2A architectures.
- Knowledge graph and GraphRAG experience.
- Fine-tuning experience (LoRA, QLoRA, PEFT).
- Experience deploying AI systems in healthcare, finance, legal, insurance, or cybersecurity domains.
Sign Ideal Candidate Profile
The strongest candidates for either role should be able to confidently discuss:
- Agent architecture tradeoffs
- Retrieval evaluation methodologies
- Hallucination measurement
- LLM evaluation frameworks
- Fine-tuning vs RAG decisions
- Production AI failures and learnings
- Cost optimization strategies
- MCP/A2A interoperability
- AI governance and model risk management
- Scaling AI systems from prototype to enterprise production
- ificant experience operating AI systems supporting thousands to millions of users.
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