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Data Scientist (Applied AI & Prompt Engineering Senior Analyst)

Blend360
Columbia, Maryland
Full time
1 day ago

Company Description

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 Description

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. 

Qualifications

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 applicaitons 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 Information

Due to a high volume of inauthentic applications, we require a current and complete LinkedIn profile for consideration

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