AI/ML Engineer

RiskForce; Northern (KY); Hybrid; USD 120,000 - 155,000; Full time

Job summary

RiskForce is hiring an experienced ML engineer to design and build production-grade ML features, including LLM-powered applications and RAG pipelines, for our core product. You’ll shape how our platform uses large language models, retrieval-augmented generation, and intelligent automation across the full lifecycle.

You will collaborate with product and engineering teams to ship reliable AI capabilities, optimize costs, and implement evaluation frameworks, guardrails, and safety measures across

Qualifications

• 3+ years of experience in machine learning or AI engineering, with hands-on experience building LLM-powered applications.
• Strong proficiency in Python with production-level software engineering practices
• Experience building RAG systems with vector databases and document processing pipelines
• Solid understanding of NLP fundamentals, prompt engineering, function calling, and tool-use patterns
• Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, or equivalent
• Experience with cloud platforms (AWS, Azure, or GCP) for deploying and scaling ML workloads
• Ability to design evaluation criteria and measure AI output quality systematically

Responsibilities

• Design and implement LLM-powered features and intelligent workflows that solve real customer problems
• Build and optimize retrieval-augmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding models, and vector search
• Develop and refine prompt engineering strategies across multiple foundation models and use cases
• Create evaluation frameworks and benchmarking infrastructure to systematically measure model performance, accuracy, and cost-effectiveness
• Implement guardrails, output validation, and safety filtering to ensure reliable and trustworthy AI behavior in production
• Monitor and optimize token usage, latency, and inference costs across multi-model architectures
• Collaborate closely with product and engineering teams to identify high-impact AI opportunities and translate them into shipped features

Skills

ML/AI engineering experience

Python production-grade

RAG systems with vector DBs

NLP fundamentals

Prompt engineering & function calling

LLM orchestration frameworks

Cloud platforms (AWS/GCP/Azure)

Tools

Pinecone

Weaviate

pgvector

LangChain

LlamaIndex

Job description

About the Role

Join our engineering team at the forefront of applied AI to design and build production-grade ML features that power the core product experience. You'll shape the future of how our platform leverages large language models, retrieval-augmented generation, and intelligent automation — working across the full lifecycle from rapid prototyping to reliable, scalable production systems.

What You'll Do

• Design and implement LLM-powered features and intelligent workflows that solve real customer problems
• Build and optimize retrieval-augmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding models, and vector search
• Develop and refine prompt engineering strategies across multiple foundation models and use cases
• Create evaluation frameworks and benchmarking infrastructure to systematically measure model performance, accuracy, and cost-effectiveness
• Implement guardrails, output validation, and safety filtering to ensure reliable and trustworthy AI behavior in production
• Monitor and optimize token usage, latency, and inference costs across multi-model architectures
• Collaborate closely with product and engineering teams to identify high-impact AI opportunities and translate them into shipped features

Requirements

• 3+ years of experience in machine learning or AI engineering, with hands-on experience building LLM-powered applications
• Strong proficiency in Python with production-level software engineering practices
• Experience building RAG systems with vector databases (Pinecone, Weaviate, pgvector, or similar) and document processing pipelines
• Solid understanding of NLP fundamentals, prompt engineering, function calling, and tool-use patterns
• Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, or equivalent
• Experience with cloud platforms (AWS, Azure, or GCP) for deploying and scaling ML workloads
• Ability to design evaluation criteria and measure AI output quality systematically

Nice to Have

• Experience building AI systems in regulated, high-security, or compliance-driven environments
• Background in MLOps — model versioning, experiment tracking, CI/CD for ML pipelines
• Hands-on experience fine-tuning or distilling open-source models (LLaMA, Mistral, etc.)
• Experience with multi-agent frameworks, autonomous agent architectures, or tool-use orchestration
• Published research or technical writing in NLP, information retrieval, or applied ML