Machine Learning Engineer (Agentic AI Platform) Job at Barker Staffing Solutions

About the Role

We're building the next generation of agentic AI systems, intelligent, autonomous agents that reason, act, and continuously improve. As a Machine Learning Engineer, you won't just build models, you'll architect the entire ecosystem where our AI agents live, learn, and operate.

This is a high-impact role for a product-minded, systems-level thinker who thrives in ambiguity and wants to shape foundational AI infrastructure from the ground up.

You'll work at the intersection of LLMs, distributed systems, and real-world applications, owning everything from core ML architecture to customer-facing experiences.

What You'll Do

• Architect & Build Agentic Systems
  
  
  
  • Design and develop our core agentic AI platform, enabling autonomous reasoning, decision-making, and continuous learning
  
  
  • Implement multi-agent orchestration frameworks (e.g., LangGraph)
  
  
  
  


• Own the ML & Data Infrastructure
  
  
  
  • Architect a modern lakehouse-based data platform
  
  
  • Build scalable data pipelines, feature stores, and real-time ML serving systems
  
  
  
  


• Develop LLM-Powered Applications
  
  
  
  • Build and optimize RAG systems, prompt pipelines, and reasoning workflows
  
  
  • Develop customer-facing applications, including a seamless AI chat interface
  
  
  
  


• Build Tool Machines for Agents
  
  
  
  • Create reliable, safe, and extensible tools that allow agents to interact with external systems, APIs, and data sources
  
  
  
  


• Drive MLOps & Model Lifecycle
  
  
  
  • Partner with data scientists to design infrastructure for training, fine-tuning, evaluation, and deployment
  
  
  • Implement robust experimentation, monitoring, and feedback loops
  
  
  
  


• Ship Production-Grade Systems
  
  
  
  • Write high-quality, scalable Python code
  
  
  • Ensure reliability, observability, and performance across distributed systems
  
  
  
  

What We're Looking For

Core Requirements

• 3–8 years of experience in Machine Learning Engineering or Software Engineering (ML-focused)


• Strong production experience with Python


• Hands‑on experience with:
  
  
  
  • ML frameworks (e.g., PyTorch, TensorFlow)
  
  
  • LLMs, agentic frameworks (e.g., LangGraph), or RAG systems
  
  
  
  


• Experience designing scalable ML systems (training + serving)

Preferred Background

• Experience at top‑tier tech companies (e.g., Meta, Google, Reddit, Pinterest)


• Combined experience across Big Tech + high‑growth startup environments


• Background in ads, search, recommendation systems, or large‑scale ML platforms


• Prior experience at a venture‑backed startup

Nice to Have

• MLOps and infrastructure experience:
  
  
  
  • Kubernetes, MLflow, model serving systems
  
  
  
  


• Data engineering experience:
  
  
  
  • Spark, Airflow, dbt, ETL/streaming pipelines
  
  
  
  


• Experience designing systems using lakehouse architectures

Education

• Master's or PhD in Computer Science (or related field), OR


• Bachelor's degree + strong professional experience in software/ML engineering

Tech Stack

• Languages & Frameworks: Python, PyTorch, TensorFlow


• AI/LLM: LangGraph, RAG architectures


• Infrastructure: Kubernetes, MLflow


• Data: Spark, Airflow, dbt, lakehouse architecture

Who You Are

• Product-minded: You think about user experience, not just models


• Systems thinker: You design for scale, reliability, and extensibility


• Builder: You ship fast, iterate quickly, and thrive in ambiguity


• Impact-driven: You want to own and shape foundational technology

What Success Looks Like

• You've built scalable systems powering autonomous AI agents in production


• You've improved model performance and reliability through robust infrastructure and feedback loops


• You've delivered end‑to‑end ML products used by real customers

Why Join Us

• Build cutting‑edge agentic AI systems from the ground up


• Own foundational architecture across the entire AI stack


• Work alongside a team operating at the intersection of LLMs, infrastructure, and product


• Massive opportunity for ownership, impact, and growth