Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com
Job DescriptionWe’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.
Experience: 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.
Additional Information
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. It is not typical for an individual to be hired at or near the top of the range and determining factors for compensation are considered for each individual circumstance. BLEND360 also offers a competitive benefits program to meet the health and financial well-being of our team and their families. You can look forward to a range of benefits including medical, dental, vision, 401K, PTO, paid holidays, commuter benefits, spending accounts, life insurance, disability coverage, and EAPs.
Skills Required
- 3+ years of applied machine learning experience focused on NLP, transformers, or generative AI systems
- Hands-on experience with LLM libraries such as LangChain, LlamaIndex, OpenAI API, CrewAI, or similar
- Experience with LLM services such as Azure Prompt Flow, AWS Bedrock agents, or similar
- Experience designing multi-step agentic systems with tool or API calls and safeguards against errors, loops, and unsafe tool use
- Experience building and deploying machine learning models to production through API, batch, or streaming architectures
- Fluency in Python and production-grade coding practices
- Ability to design and analyze machine learning experiments using performance metrics
- Ability to develop, deploy, and monitor AI applications in AWS, Azure, GCP, or similar cloud environments
- Familiarity with containerization, versioning, and CI/CD
- Experience implementing privacy, bias mitigation, safety guardrails, or related responsible AI practices
- 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
- Strong collaboration at the intersection of data science and engineering
Blend360 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.
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Fair & Transparent Compensation — Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
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Flexible Benefits — Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
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Retirement Support — A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.
Blend360 Insights
What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.
