AI/ML Engineer

RiskForce LLC; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

RiskForce LLC is seeking an experienced AI engineer to shape production-grade ML features across the full lifecycle, from prototyping to scalable deployment. You will work on LLM-powered applications, RAG pipelines, prompt strategies, and evaluation frameworks.

The role emphasizes safe, cost-aware AI, multi-model orchestration, and collaboration with product teams to deliver high-impact capabilities.

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 (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.

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

LLM development

Python production

RAG systems

Vector databases

NLP fundamentals

Prompt engineering

LangChain / LlamaIndex

Cloud deployment

Model evaluation

Tools

Pinecone

Weaviate

pgvector

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