Artificial Intelligence Engineer

Senior Software Engineer — AI/ML

Plano, TX | On-site

About the Company

A tier-one financial services enterprise is building next-generation AI infrastructure to power intelligent conversational systems—from chatbots handling routine transactions to voicebots managing complex workflows. They're scaling from proof-of-concept to production systems handling millions of interactions monthly.

About the Role

This is a senior engineering role focused on shipping robust, reliable AI applications on AWS, with direct impact on how millions of customers interact with financial services. You'll own the full stack: from designing RAG pipelines and prompt strategies, to orchestrating LLM workflows, to deploying containerized services that run 24/7. You'll work with leading models (including Claude via AWS Bedrock), define guardrails and responsible AI practices, and mentor mid-level engineers on production AI patterns.

Responsibilities

• Design and build production RAG systems — from chunking strategy and embedding model selection, to vector store architecture (pgvector, OpenSearch, Pinecone, or FAISS), to retrieval optimization for accuracy and latency
• Orchestrate complex LLM workflows using frameworks like LangChain, LlamaIndex, or Semantic Kernel; implement multi-step reasoning, tool use, and structured output patterns
• Develop conversational AI systems across text (chatbots) and speech (voicebots with STT/TTS integration); manage latency and streaming constraints in real-time voice
• Leverage AWS Bedrock and Claude models directly; design effective system prompts, few-shot examples, and chain-of-thought reasoning; iterate based on real-world performance
• Build and scale AWS infrastructure — Lambda functions, API Gateway, Step Functions, DynamoDB, SQS, S3; implement CI/CD pipelines with Docker/ECS or EKS; write infrastructure-as-code
• Define quality and safety guardrails — implement content filtering, responsible AI practices, and evaluation frameworks to catch model drift and output degradation before production impact
• Own API design (REST, GraphQL) and microservices architecture; build event-driven systems that integrate seamlessly with enterprise systems
• Mentor and unblock junior engineers; establish patterns and best practices for how your team ships AI features reliably

Qualifications

• 10+ years shipping production software in Python or Java (Python strongly preferred)
• 2+ years hands-on building and deploying AI/ML applications in production — not coursework, not personal projects; real systems in a real environment
• Deep RAG expertise — you've built chunking strategies, selected and tuned embedding models, chosen vector stores based on use case, and optimized retrieval for production
• Proficiency with AI orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or CrewAI); you can explain the trade-offs between them and when to use each
• Hands-on AWS Bedrock and Claude experience — direct invocation, token counting, structured outputs, prompt optimization; familiarity with other LLM APIs (OpenAI, Azure) is transferable but AWS Bedrock depth is expected
• Advanced prompt engineering skills — system prompts, few-shot learning, chain-of-thought reasoning, tool use, and structured outputs aren't abstract concepts to you; you've debugged them in production
• Conversational AI systems — you've shipped text-based chatbots AND ideally have experience with voice systems (speech-to-text, text-to-speech, real-time streaming)
• Senior AWS proficiency — Lambda, API Gateway, Step Functions, DynamoDB, SQS, S3; you build without hand-holding and reason through cost and performance trade-offs
• CI/CD and containerization — solid grasp of Docker, ECS/EKS, infrastructure-as-code; shipping changes frequently and safely is table stakes
• API and systems design — you can articulate why you chose REST vs. GraphQL, how events flow through microservices, and where synchronous vs. asynchronous makes sense
• Evaluation and safety mindset — you've built evaluation frameworks for LLM outputs, implemented guardrails, and thought deeply about what can go wrong at scale

Preferred Skills

• Experience with model fine-tuning or retrieval optimization (RAG vs. in-context learning trade-offs)
• GraphQL implementation in production
• Familiarity with additional vector stores (Weaviate, Chroma, pgvector)
• Background in financial services or regulated industries
• Published writing or talks on AI systems architecture
• Experience with multi-modal models or agent frameworks beyond basic tool use

Pay range and compensation package

Competitive salary, equity, and benefits package commensurate with experience.

Equal Opportunity Statement

We're committed to building a diverse team and welcome applications from underrepresented groups in engineering. We do not discriminate based on race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or any other protected characteristic.

How to Apply

If this role fits your background and you're ready to ship production AI systems at scale, please apply with:

• Your resume — include 2–3 specific examples of AI/ML production work (system, your role, outcome)
• Optional: Link to GitHub, blog, or publicly shipped work

We review applications on a rolling basis and move quickly with serious candidates.