About the Company
Translucent AI is on a mission to revolutionize administrative healthcare AI solutions at scale, while building our platform infrastructure and applications with the highest quality and security standards. As leaders in AI-driven technology, we prioritize confidentiality, integrity, and availability across our systems. At Translucent, we are building secure, state-of-the-art solutions that push the boundaries of AI and the healthcare industry, empowering our clients with transformative technology.
About the Job
We’re looking for a versatile Fullstack AI Engineer with a passion for building and integrating end-to-end Gen AI solutions. This role is critical in developing custom LLMs and specialized AI agents for different clients/tasks, requiring end-to-end ownership of projects. This is a unique opportunity to work on a range of projects, taking a hands-on approach to both the core AI engineering and application development, and have a direct impact on our technology roadmap, aligning your work with core business and customer needs.
Our ideal candidate is either an AI Engineer, Machine Learning Engineer, Data Scientist, Data Engineer, or FullStack software engineer who wants to transition or focus on end-to-end GenAI and agentic workflows. What You’ll Do
- Build GenOps pipelines and robust AI frameworks. Construct datasets, train & fine-tune and evaluate GenAI models, and collaborate with other team members through the entire lifecycle of different GenAI solutions from scratch to production.
- Design, implement, and maintain agentic workflows for Conversational AI and task automation of both customer-facing applications, R&D, and internal tooling.
- Design, implement, and maintain Document AI pipelines that use generative AI for document processing, including OCR, form parsing, text extraction and summarization, and classification tasks.
- Deploy GenAI models on different cloud providers as well as on-prem, and set up endpoints for foundational models. You will work with different cloud providers (experience with Google Cloud is a plus, while some projects will require working with AWS, Azure OpenAI, and other cloud providers)
- Work closely with other software engineers, machine learning engineers, product and business stakeholders.
- Produce technical documentation and engage in continuous learning to master Generative AI technologies, best practices, and share your knowledge and achievements with your colleagues.
What You’ll Need
- Experience in software and AI engineering, with a master’s degree in AI or ML, Computer Science, Data Science, or equivalent relevant professional experience in the field.
- Fundamental understanding of software engineering (experience building full-stack web applications is a plus) and the web (REST APIs, client-server communication, databases)
- Proficiency with Generative AI and Natural Language Processing
- Experience with LLMs and Text Generation
- Experience with foundational models and LLM Prompt Engineering & Fine-Tuning for custom tasks.
- Experience with Retrieval Augmented Generation (RAG)
- Experience in constructing custom, relevant quality datasets for performing LLM fine-tuning and RAG.
- Experience and/or a fundamental understanding of Natural Language Processing, tokenization / text encoding & text generation, and text-based models
- A fundamental understanding of attention mechanisms and transformer architecture.
- Experience designing and implementing software features, applications, and solutions using Generative AI and LLMs.
- Experience with vector databases, semantic search, indexing techniques, and enhancing AI agent’s long-term memory
- Experience developing agentic workflows and general GenAI solutions with the LangChain framework.
- Experience with working with GenAI solutions in a production/production-like environment, including applying techniques for data collection, prompt execution, maintenance, and orchestration.
- Knowledge of Conversational AI system architecture using Finite State Machine dialog models and multi-agent systems.
- Experience or applied knowledge of Responsible AI & AI Ethics, conducting AI Impact assessments, analyzing use cases from both a product and technical standpoint to determine adequate model selection and success metrics, bias identification and mitigation techniques.
- Experience or interest in learning about AI for Accessibility
- Experience or a fundamental understanding of the GenOps process
Location
This is a fully remote role within U.S. territories. Traveling to company outings and other special events can be expected, but rarely required, once every four to six months.
Eligible candidates can work from the following states:
Alabama, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming.
Resources
At Translucent AI, continuous learning is the status quo. The following resources can help you prepare for this role and interview with Translucent AI.
