Lead AI Engineer

About the Role

We're growing our Data Science team to drive innovation in Generative AI. As a Data Scientist – Applied AI & Prompt Engineering, you will design, build, and deploy LLM-powered solutions that directly impact our products and users. You'll work hands-on with LLMs, transformers, retrieval-augmented generation (RAG), and AI agents, collaborating with Engineering to bring prototypes all the way to production and take ownership of their ongoing maintenance, enhancement, and evolution.

What You'll Do

• Architect and implement production-ready AI solutions involving LLMs, transformer-based models, retrieval systems, agentic workflows, and AI agents for generative tasks and automation
• Design and iterate on prompts, workflows, and RAG pipelines to improve accuracy, cost-efficiency, latency, and safety
• Design and build multi-step agentic systems that break down complex tasks, invoke external tools or APIs, manage state, and handle reasoning chains robustly
• Deploy models and GenAI pipelines in production environments (API, batch, streaming), ensuring reliability and scalability
• Build and maintain evaluation frameworks to measure model grounding, factuality, latency, and cost
• Develop and integrate guardrails (e.g., prompt-injection protections, content moderation, output validation), and safeguards for agent loops (e.g., loop prevention, tool call limits, state validation)
• Collaborate cross-functionally with Product, Engineering, and ML Ops to deliver high-quality AI features end-to-end

Qualifications

• 3+ years applied machine learning, with hands-on focus on NLP, transformers, or generative AI systems
• LLM and Agent Tools: Hands-on experience with LLM-related libraries (e.g. LangChain, LlamaIndex, OpenAI API, CrewAI, or similar) and services (Azure Prompt flow, AWS Bedrock agents, or similar)
• Agentic Systems: Experience designing multi-step agents that combine LLM reasoning with tool/API calls, with safeguards against errors, loops, and unsafe tool use
• ML Foundations: Proven experience building and deploying machine learning models to production (API, batch, or streaming)
• Coding: Fluency in Python, with clean, modular, production-grade code practices
• Experimentation: Strong ability to design and analyze ML experiments; track performance using metrics, not gut feel
• Deployment: Ability to develop, deploy and monitor AI-powered applications in cloud environments (e.g. AWS, Azure, GCP) using APIs, batch, or streaming architectures. Familiarity with containerization, versioning, and CI/CD
• Responsible AI: Experience implementing privacy, bias mitigation, safety guardrails, or related practices

Additional Qualifications

• Degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience)
• Expertise in transformer-based models and LLM architectures
• Ability to bridge rapid prototyping and production deployment — you own what you build through to live systems
• Strong collaborator who thrives at the intersection of DS + Engineering

Pay

The starting pay range for this role is $125,000 - $180,000. Actual compensation within the range will be dependent on several factors including but not limited to relevant experience, skills, certifications, training, and location.

Benefits

BLEND360 offers a competitive benefits program including medical, dental, vision, 401K, PTO, paid holidays, commuter benefits, spending accounts, life insurance, disability coverage, and EAPs.