Senior AI Solution Engineer - LLM & RAG

Artmac Soft is a technology consulting and service‑oriented IT company that provides innovative technology solutions and services to customers.

Job Type: W2/C2C | Location: Prosper, Texas | Experience Required: 8+ years

Responsibilities

• Design and develop AI‑powered applications using LLMs, Generative AI, and Retrieval‑Augmented Generation (RAG) architectures.
• Build scalable solutions with Python, LLM APIs, REST APIs, and enterprise integrations.
• Develop RAG pipelines including document ingestion, chunking, embeddings, vector search, retrieval, reranking, and response generation.
• Integrate LLM solutions with enterprise applications, databases, APIs, and external systems.
• Evaluate and select appropriate LLMs, models, embedding technologies, and AI frameworks based on business requirements.
• Create proofs of concept (POCs), prototypes, demos, and minimum viable solutions for new AI use cases.
• Translate business requirements into technical designs and working AI solutions.
• Collaborate with clients, product teams, architects, developers, and business stakeholders to understand challenges and define AI‑driven solutions.
• Troubleshoot and optimize AI applications for accuracy, performance, scalability, latency, and cost.

Requirements

• 8+ years of software engineering / AI engineering experience.
• Strong hands‑on experience with Python.
• Experience working with LLM APIs such as OpenAI‑compatible APIs, Azure OpenAI, Anthropic, Gemini, or similar platforms.
• Familiarity with AI/LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent.
• Experience developing AI prototypes and converting successful POCs into production‑ready solutions.
• Knowledge of LLM evaluation, observability, and guardrails.
• Proficiency with Docker, Kubernetes, CI/CD, Git, and cloud platforms.
• Experience integrating AI solutions with SQL/NoSQL databases, enterprise applications, CRM, ERP, or business platforms.
• Hands‑on experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or other enterprise AI platforms.
• Strong understanding of Retrieval‑Augmented Generation (RAG) architectures and implementation.
• Experience building and consuming REST APIs and enterprise integrations.
• Experience with vector databases/search technologies such as Pinecone, Weaviate, Qdrant, FAISS, Azure AI Search, or equivalent.
• Strong understanding of embeddings, semantic search, document processing, and information retrieval.
• Knowledge of AI agents, tool calling, function calling, and multi‑agent architectures.

Qualifications

• Bachelor’s degree or equivalent combination of education and experience.