AI/ML Engineer

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.