Principal Machine Learning Engineer – Agentic AI Platforms

Principal Machine Learning Engineer – Agentic AI Platforms | JPC-8589

USC/GC/H4-EAD- W2Need LinkedIn, VISA DL100% remote

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, GCP)

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.

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
• Significant experience operating AI systems supporting thousands to millions of users.

$000,000 – $000,000 Salary range is not shown on the candidate portal