AI/ML Engineer

Overview

Hybrid
Depends on Experience
Contract - W2
Contract - 12 Month(s)
50% Travel
Able to Provide Sponsorship

Skills

Artificial Intelligence
Data Processing
Database Performance Tuning
Design Patterns
Evaluation
GPU
Machine Learning (ML)
Machine Learning Operations (ML Ops)
Natural Language Processing
Neo4j
Optical Character Recognition
Optimization
Orchestration
Prompt Engineering
Python
Reasoning
Use Cases
Workflow
Software Engineering
Software Design
Resource Description Framework

Job Details

Job Title: AI/ML Engineer

Location: Charlotte, NC (Hybrid)

Employment Type: W2 Only (No C2C/1099)

Duration: 12+ Months

About the Role

We are seeking a highly skilled AI/ML Engineer to design, build, and optimize advanced AI systems leveraging Retrieval-Augmented Generation (RAG), multi-modal models, and agentic architectures. This role combines deep technical expertise with strategic vision to deliver production-grade AI solutions at scale. You will work on cutting-edge technologies, including large-scale knowledge bases, vector search, and autonomous AI agents, driving innovation end-to-end.

Key Responsibilities

  • Architect & Optimize RAG Pipelines: Build complex RAG systems for multi-domain knowledge bases, TB-scale datasets, and advanced chunking/indexing strategies.
  • Lead Multi-Modal AI Experiments: Develop solutions across text, image, and diagram-based use cases.
  • Knowledge Graph Solutions: Design and maintain graph-driven retrieval and reasoning systems.
  • Agentic AI Systems: Create autonomous, task-oriented agents with tool augmentation beyond conversational AI.
  • Research & Evaluation: Stay ahead of emerging AI techniques; define evaluation frameworks for performance, safety, and reliability.
  • Observability & Monitoring: Implement telemetry, tracing, and error detection for LLM-driven workflows.
  • Hands-On Development: Work with OpenAI APIs and similar platforms; apply prompt engineering and LLM design patterns.
  • Software Engineering: Develop robust Python-based solutions for data processing, orchestration, and integration.
  • Vector Database Optimization: Leverage vector stores for semantic search and scalable indexing.

Qualifications

  • Experience: 8+ years in software engineering or applied ML.
  • Proven track record in production-scale RAG systems.
  • Strong knowledge of embeddings, vector similarity search, and retrieval optimization.
  • Hands-on experience with multi-modal models (VLMs, OCR, vision-language reasoning).
  • Familiarity with knowledge graphs (Neo4j, RDF, graph embeddings).
  • Prior work on agentic/LLM-driven systems (tool use, planning, function calling).
  • Advanced Python engineering skills and modern development practices.
  • Experience with AI observability frameworks, experiment tracking, and evaluation tooling.
  • Proficiency with OpenAI APIs or similar LLM platforms.
  • Ability to translate cutting-edge research into practical solutions.

Nice to Have

  • Experience with distributed systems and high-volume data processing.
  • Background in ML Ops, GPU orchestration, or model deployment pipelines.
  • Expertise in search systems, IR, or NLP.

 

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