Senior AI Engineer – LLM Systems & RAG Optimization
Senior AI Engineer – LLM Systems & RAG Optimization
Location: Remote (Global) Type: Full-Time or Contract Company: Texas Sports Academy
About Us
Texas Sports Academy is building the future of education for athletes. We combine elite athletic training with serious academics — and we’re scaling rapidly.
We already have a parent-facing AI SMS chatbot live in production. It works.
Now we need someone exceptional to make it world-class.
This is not a “call the OpenAI API” role. This is a systems, evaluation, and scaling role.
What You’ll Own
You will improve and scale our parent SMS AI chatbot used by thousands of families.
This includes:
• Optimizing and redesigning our RAG architecture
• Reducing hallucinations and irrelevant retrieval
• Improving latency and token efficiency
• Building robust evaluation pipelines
• Implementing LLM observability and monitoring
• Designing cost-efficient scaling infrastructure
• Improving conversation memory and routing logic
• Hardening guardrails for real-world usage
You will operate as the AI systems architect for this product.
Required Experience
You must have real production experience building and scaling LLM systems.
We’re looking for:
• 3+ years in applied ML or NLP
• Strong Python skills
• Deep experience with RAG systems
• Experience with vector databases (Pinecone, Weaviate, FAISS, etc.)
• Experience optimizing chunking & embedding strategies
• Experience evaluating LLM systems beyond subjective review
• Familiarity with LLM observability tools (LangSmith, Helicone, PromptLayer, etc.)
• Experience deploying scalable AI systems in production
You should be able to:
• Architect a full RAG pipeline from scratch
• Diagnose retrieval failures
• Build evaluation datasets
• Optimize for cost at scale
• Explain tradeoffs clearly
Bonus Points
• Experience with SMS-based AI systems
• Experience with multi-model routing
• Experience with context compression
• Startup experience
• Strong system design background
What Success Looks Like
Within 90 days:
• Hallucination rate meaningfully reduced
• Retrieval accuracy measurably improved
• Cost per conversation reduced
• Clear evaluation metrics implemented
• Observability dashboard live
• Scalable architecture roadmap in place
Take-Home Evaluation
You’ll be asked to:
Audit our existing SMS chatbot and deliver a structured improvement plan including:
• Architecture critique
• Retrieval optimization suggestions
• Hallucination reduction strategies
• Scaling plan
• Metrics & evaluation framework
• 30 / 60 / 90-day roadmap
Time expectation: 4–6 hours.
Compensation
Competitive. Open to global talent. Contract or full-time available.
If you’ve built real AI systems — not demos — we want to talk.
Job Type: Full-time
Pay: $250,000.00 - $300,000.00 per year
Work Location: Remote
Apply Now
Apply Now
Location: Remote (Global) Type: Full-Time or Contract Company: Texas Sports Academy
About Us
Texas Sports Academy is building the future of education for athletes. We combine elite athletic training with serious academics — and we’re scaling rapidly.
We already have a parent-facing AI SMS chatbot live in production. It works.
Now we need someone exceptional to make it world-class.
This is not a “call the OpenAI API” role. This is a systems, evaluation, and scaling role.
What You’ll Own
You will improve and scale our parent SMS AI chatbot used by thousands of families.
This includes:
• Optimizing and redesigning our RAG architecture
• Reducing hallucinations and irrelevant retrieval
• Improving latency and token efficiency
• Building robust evaluation pipelines
• Implementing LLM observability and monitoring
• Designing cost-efficient scaling infrastructure
• Improving conversation memory and routing logic
• Hardening guardrails for real-world usage
You will operate as the AI systems architect for this product.
Required Experience
You must have real production experience building and scaling LLM systems.
We’re looking for:
• 3+ years in applied ML or NLP
• Strong Python skills
• Deep experience with RAG systems
• Experience with vector databases (Pinecone, Weaviate, FAISS, etc.)
• Experience optimizing chunking & embedding strategies
• Experience evaluating LLM systems beyond subjective review
• Familiarity with LLM observability tools (LangSmith, Helicone, PromptLayer, etc.)
• Experience deploying scalable AI systems in production
You should be able to:
• Architect a full RAG pipeline from scratch
• Diagnose retrieval failures
• Build evaluation datasets
• Optimize for cost at scale
• Explain tradeoffs clearly
Bonus Points
• Experience with SMS-based AI systems
• Experience with multi-model routing
• Experience with context compression
• Startup experience
• Strong system design background
What Success Looks Like
Within 90 days:
• Hallucination rate meaningfully reduced
• Retrieval accuracy measurably improved
• Cost per conversation reduced
• Clear evaluation metrics implemented
• Observability dashboard live
• Scalable architecture roadmap in place
Take-Home Evaluation
You’ll be asked to:
Audit our existing SMS chatbot and deliver a structured improvement plan including:
• Architecture critique
• Retrieval optimization suggestions
• Hallucination reduction strategies
• Scaling plan
• Metrics & evaluation framework
• 30 / 60 / 90-day roadmap
Time expectation: 4–6 hours.
Compensation
Competitive. Open to global talent. Contract or full-time available.
If you’ve built real AI systems — not demos — we want to talk.
Job Type: Full-time
Pay: $250,000.00 - $300,000.00 per year
Work Location: Remote
Apply Now
Apply Now