Full Stack AI Engineer

We have been retained by our client in Dallas, Texas, a leader in AI and analytics, to deliver a Full Stack AI Engineer on a regular full-time, direct-hire basis. Work onsite Dallas location. Provide full-stack AI and analytics services & solutions to empower systems that achieve real outcomes and value at scale. This team is on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. One purpose is to improve certainty that the guardrails implemented can provide.

We are looking for a highly skilled Full Stack AI Engineer with 5-7+ years of experience in software engineering, with a heavy focus on Python, FastAPI, Azure infrastructure, and AI systems. The ideal candidate has experience building high-performance API services and implementing complex RAG and Agentic AI architectures., * Experience: Minimum of 5-7+ years of professional experience in software development including some AI software engineering experience.

• API & Backend: Expert in building high-performance API / microservices using Python (FastAPI) deployed on Azure.
• AI Integration: Hands-on experience integrating Generative AI/LLMs, APIs, Azure and other model providers.
• Infrastructure & DevOps: Experience with DevOps, CI/CD pipelines, and ML pipelines within the Azure.
• Agentic AI: Exposure to building Gen AI/Agentic AI applications, managing efficiency, latency, and backend infrastructure.
• Technical Standards: Strong Python programming skills with a deep understanding of OpenAI API standards, FastAPI, JSON RESTful design, and LLM orchestration.
• Preferred Skills: Experience working with any the following is desired: RAG OR ElevenLabs OR VoiceBot OR scikit-learn OR XGBoost OR SciPy OR PyTorch OR TensorFLow OR Ollama OR LLM OR vLLM OR Hallucinations OR Prompt Engineering OR Azure Foundry models

Core Focus Areas & Expectations

Candidates will be expected to demonstrate technical proficiency in the following areas:

1. Retrieval-Augmented Generation (RAG)

• Ability to design and implement end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators.
• Expertise in latency optimization and relevance tuning to ensure production-grade performance.
• Strategic approach to document chunking and embedding, balancing granularity with semantic coherence.

2. Agent Development

• Practical experience developing autonomous or semi-autonomous agents using frameworks such as LangChain, CrewAI, or Semantic Kernel.
• Ability to manage orchestration, tool integration, and robust error handling for non-deterministic AI outputs.
• Proficiency in managing memory and context (episodic vs. long-term) in multi-turn interactions and external API interfacing.

3. Evaluation and Optimization

• Familiarity with evaluation frameworks (e.g., RAGAS, TruLens) to assess performance, grounding accuracy, and hallucination detection.
• Ability to iterate systems based on performance metrics and continuous improvement practices.