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# Machine Learning Engineer

- **Company:** CareerCircle
- **Location:** Raleigh, NC, United States (Remote available)
- **Experience:** Expert
- **Salary:** $156,000.0 - $176,800.0
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Automation of Tests, Microsoft Azure, Cloud Computing, Databases, Continuous Integration, Graph Database, Information Retrieval, Python (Programming Language), Machine Learning, Metadata, Language Modeling, Natural Language Processing, Search Technologies, Software Engineering, SQL Databases, Workflow Management Systems, Enterprise Search, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Generative AI, AI Platforms, Kubernetes, Information Technology, Low Latency, Machine Learning Operations, Virtual Agents, Automation Anywhere, Docker
- **Published:** September 4, 2026
- **Apply:** https://www.careercircle.com/jobs/all/all/usa/nc/raleigh/0cf16693-3918-42ba-af15-265842703bcc

## About the Role

Planning
Equities
Chunking
Pipelines
Operations
Management
Innovation
Algorithms
Agentic AI
Scalability
Reliability
Data Science
Communication, Observability
Shared Memory, Masters degree in Computer Science, Machine Learning, Data Science, Engineering, or related discipline.

 - 10+ years of software engineering, machine learning engineering, or applied AI experience.

 - 3+ years building production AI, LLM, or Generative AI solutions.

 - Strong Python development experience.

 - Experience developing production-grade Retrieval-Augmented Generation (RAG) systems.

 - Experience building or supporting Agentic AI, AI orchestration, or multi-agent workflows., Bachelor's degree in Computer Science, Machine Learning, Data Science, Engineering, or related discipline.

6+ years of software engineering, machine learning engineering, or applied AI experience.

3+ years building production AI, LLM, or Generative AI solutions.

Strong Python development experience.

Experience developing production-grade Retrieval-Augmented Generation (RAG) systems.

Experience building or supporting Agentic AI, AI orchestration, or multi-agent workflows.

Strong understanding of:

Large Language Models (LLMs)

Natural Language Processing (NLP)

Information Retrieval

Semantic Search

Embeddings

Prompt Engineering

Model Evaluation

Experience working with vector databases and retrieval platforms.

Strong software engineering fundamentals including testing, CI/CD, observability, and scalable system design.

Preferred Qualifications

Experience with agent frameworks such as:

LangGraph

AutoGen

CrewAI

Semantic Kernel

OpenAI Agents SDK

LlamaIndex Workflows

Experience implementing:

Multi-agent communication patterns

Shared memory architectures

Tool-calling agents

Autonomous workflows

Human-in-the-loop systems

Experience with cloud platforms including AWS, Azure, or GCP.

Familiarity with:

Kubernetes

Docker

MLOps

LLMOps

AI observability platforms

Knowledge graph or enterprise search experience.

Experience building AI systems in highly regulated or knowledge-intensive domains.

Skills

Python, Data science, Machine learning, Sql, Algorithm, Aws, agentic ai, mcp, Data, Predictive modelling, Artificial intelligence

Top Skills Details

Python,Data science,Machine learning,Sql,Algorithm,Aws,agentic ai,mcp, Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.

## Description

Microsoft Azure, Vector Database
Computer Science
Machine Learning
Enterprise Search
Business Valuation
Prompt Engineering
Workflow Management
Amazon Web Services
Predictive Modeling
Multi-Agent Systems
Software Engineering
Technology Ecosystems
Full Stack Development
Artificial Intelligence
Large Language Modeling
Business Transformation
SQL (Programming Language)
Python (Programming Language)
Continuous Improvement Process
Retrieval Augmented Generation
Microsoft Certified Professional
Natural Language Processing (NLP)
Generative Artificial Intelligence
Artificial Intelligence Infrastructure
Applications Of Artificial Intelligence, Our client is seeking a Senior Machine Learning Engineer II to help build and scale advanced Agentic AI and Multi-Agent Systems that transform how professionals interact with information.

This role goes beyond traditional machine learning engineering. The ideal candidate will have experience designing intelligent AI systems capable of reasoning, planning, retrieval, tool utilization, workflow orchestration, and autonomous task execution across large-scale knowledge environments. You will work closely with Applied Scientists, ML Engineers, Architects, Product Leaders, and Software Engineers to develop production-grade AI systems that leverage Large Language Models (LLMs), RAG architectures, vector search, agent frameworks, and emerging reasoning technologies.

This position is ideal for engineers who have moved beyond basic prompt engineering and have experience building sophisticated AI applications capable of solving complex, multi-step business problems.

Key Responsibilities

Agentic AI Development

Design, build, and deploy production-scale multi-agent AI systems.

Develop agent workflows capable of planning, reasoning, retrieval, tool utilization, validation, and task execution., Implement shared memory, state management, context preservation, and agent communication frameworks.

Develop guardrails and validation systems to improve reliability and reduce hallucinations.

Retrieval-Augmented Generation (RAG)

Design and optimize enterprise-scale RAG architectures.

Develop advanced retrieval strategies leveraging:

Vector databases

Semantic search

Knowledge graphs

Metadata filtering

Hybrid retrieval approaches

Improve grounding, citation accuracy, retrieval quality, and relevance.

Optimize chunking strategies, embedding pipelines, and context management.

Machine Learning Engineering

Build scalable AI and machine learning services deployed into production environments.

Develop model evaluation frameworks for both traditional machine learning and LLM-based systems.

Create automated testing pipelines for prompts, retrieval systems, agent workflows, and AI outputs.

Fine-tune, evaluate, and optimize AI systems for performance, latency, quality, and cost.

AI Evaluation & Observability

Define and measure success metrics for agentic AI systems, including:

Task completion rates

Accuracy

Hallucination rates

Retrieval effectiveness

Cost efficiency

User satisfaction

Latency

Implement monitoring, observability, and evaluation frameworks for LLM applications.

Develop processes for continuous improvement and model governance.

Research & Innovation

Evaluate emerging AI technologies and frameworks.

Investigate advances in:

Multi-Agent Systems

Agentic AI

Reasoning Models

LLM Orchestration

Knowledge Retrieval

Autonomous AI Workflows

Contribute to architecture standards and AI platform strategy.

Participate in proof-of-concepts and innovation initiatives., Planning
Equities
Chunking
Pipelines
Operations
Management
Innovation
Algorithms
Agentic AI
Scalability
Reliability
Data Science
Communication
Observability
Shared Memory
Test Automation
Microsoft Azure
Vector Database
Computer Science
Machine Learning
Enterprise Search
Business Valuation
Prompt Engineering
Workflow Management
Amazon Web Services
Predictive Modeling
Multi-Agent Systems
Software Engineering
Technology Ecosystems
Full Stack Development
Artificial Intelligence
Large Language Modeling
Business Transformation
SQL (Programming Language)
Python (Programming Language)
Continuous Improvement Process
Retrieval Augmented Generation
Microsoft Certified Professional
Natural Language Processing (NLP)


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