Job Specifications
Title: Entry-Level ML/AI Engineer
Location: Peachtree City, GA 30269 (100% REMOTE)
Contract: 6+ Months. Long-term
Industry: Automotive.
Overview
We are seeking an experienced AI Applications / DevOps Engineer to architect, implement, and optimize advanced AI solutions, with a strong focus on Large Language Models (LLMs), agentic pipelines, workflow automation, and generative AI. This role will support high-impact initiatives across engineering automation, intelligent knowledge retrieval, and autonomous, agent-driven workflows using state-of-the-art AI research and tooling.
Core Responsibilities
Design and build advanced AI-driven systems utilizing LLMs (e.g., Azure OpenAI GPT models, Claude, Llama, Mistral, Gemini, and open-source models) for text understanding, generation, summarization, and contextual reasoning within engineering workflows.
Architect and deploy agentic pipelines, including multi-agent systems, autonomous LLM agents, and chain-of-thought/reasoning systems, to enable process automation, decision support, and engineering knowledge orchestration.
Develop and implement advanced Retrieval-Augmented Generation (RAG) solutions by combining LLMs with vector databases, search engines, and enterprise knowledge sources for high-fidelity document analysis and Q&A.
Enable end-to-end automation of complex, human-in-the-loop processes by chaining LLMs, expert systems, and external tools using orchestration frameworks such as LangChain, LlamaIndex, Haystack, and CrewAI.
Evaluate, select, and integrate modern and emerging AI tools, APIs, and infrastructure, including LLMOps, vector stores, document loaders, prompt management systems, and agent frameworks.
Fine-tune, deploy, and monitor LLMs on private or in-house datasets to address domain-specific challenges while ensuring compliance and data privacy.
Stay current with the rapidly evolving AI landscape, including open-weight models, efficient architectures, guardrails, synthetic data, evaluation techniques, and multimodal models, and introduce innovative approaches into the organization.
Essential Qualifications
Bachelor’s, Master’s, or PhD in Computer Science, Artificial Intelligence, or a related field.
Deep expertise in building solutions with commercial and open-source LLMs, including prompt engineering, model selection, fine-tuning, and evaluation.
Hands-on experience developing agentic pipelines and workflow automation using frameworks such as LangChain, LlamaIndex, Semantic Kernel, and Haystack, as well as orchestrating cloud or on-prem LLM endpoints.
Proven experience designing RAG systems, including vector database management, chunking strategies, search optimization, and retrieval pipelines using technologies such as Pinecone, Weaviate, FAISS, ChromaDB, and Elasticsearch.
Working knowledge of multimodal AI (text, audio, image, diagram, and video processing), graph-based retrieval, knowledge graphs, and semantic search.
Strong Python skills and extensive experience with modern AI/ML/NLP libraries and frameworks, including Transformers, Pydantic, FastAPI, Hugging Face, and Azure OpenAI.
Experience integrating AI solutions into real-world engineering or enterprise applications, including APIs, plugins, workflow tools, agent frameworks, and MLOps/LLMOps platforms.
Familiarity with advanced prompting techniques, AI safety and guardrails, evaluation and monitoring of AI systems, and the use of synthetic data.
Direct experience with Large Language Models (LLMs), advanced Retrieval-Augmented Generation (RAG) systems, or agentic pipelines.
Hands-on experience with RAG-based applications, forecasting models, and relevant frameworks such as LangChain and LlamaIndex, along with solid machine learning and cloud deployment experience.
Preferred / Bonus Qualifications
Experience optimizing AI systems for cost, latency, reliability, and scalability in production environments.
Understanding of privacy, security, and compliance requirements in LLM and AI applications, including PII handling, access controls, and audit trails.
Experience orchestrating multi-agent and agentic workflows using frameworks such as CrewAI, AutoGen, or OpenAgents.
Familiarity with CI/CD pipelines for AI systems, containerization technologies (Docker), and cloud AI services such as Azure ML, AWS SageMaker, and GCP Vertex AI.
General Requirements
Strong critical thinking and research skills, with enthusiasm for rapid learning and experimentation with emerging AI capabilities.
Excellent communication and documentation skills.
Ability to thrive in fast-paced, highly collaborative environments with evolving requirements.