AI Engineer

About the role

AI Engineer

Location: Remote USA

Travel: Quarterly travel to Washington, DC

Position Overview

We are seeking a hands-on AI Engineer to design, develop, and deploy innovative solutions leveraging Generative AI, Agentic AI, Machine Learning, Large Language Models (LLMs), and prompt engineering. The ideal candidate will have practical experience building LLM-powered applications and agentic systems and be comfortable taking AI/ML solutions from concept and data preparation through deployment, evaluation, and production monitoring.

This role offers an opportunity to work with cutting-edge AI technologies and large, text-based datasets while solving complex business challenges in distributed and cloud environments.

Key Responsibilities

Design, build, and deploy agentic AI systems capable of autonomous decision-making, tool/function calling, planning, and multi-step task execution.

Develop end-to-end AI/ML and Generative AI solutions, including requirements analysis, data preparation, model development, deployment, and monitoring.

Build LLM-powered applications using technologies such as GPT, Claude, Gemini, Llama, and other foundation models through APIs and cloud AI platforms.

Develop Retrieval-Augmented Generation (RAG) solutions, including document retrieval, embeddings, grounding, and citation capabilities.

Design and optimize prompts using techniques such as zero-shot, few-shot, and other prompt engineering approaches.

Develop embedding pipelines and structured-output workflows for LLM applications.

Evaluate and test GenAI solutions by developing test sets, measuring accuracy, validating grounding and citations, identifying hallucinations, and applying LLM-as-judge techniques.

Design, develop, and optimize machine learning models using Python.

Apply ML and NLP techniques to problems such as classification, clustering, anomaly detection, sentiment analysis, text categorization, topic modeling, entity extraction, and summarization.

Deploy and manage AI/ML solutions in distributed and cloud environments.

Collaborate with technical and business stakeholders to understand requirements and translate business problems into scalable AI solutions.

Assess model and system design tradeoffs, including model selection, performance, scalability, cost, reliability, and production considerations.

Stay current with emerging AI technologies, frameworks, tools, and development paradigms.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.

2+ years of hands-on AI/ML engineering experience, including demonstrable experience developing LLM/Generative AI applications.

Experience building agentic AI systems, including agents with tool/function calling, planning, task decomposition, and multi-step execution.

Experience with agent frameworks such as LangGraph, CrewAI, Strands, AutoGen, or equivalent agent architectures.

Strong understanding of the modern LLM technology stack, including: Prompt engineering, RAG, Embeddings, Structured outputs, LLM APIs, Vector/search retrieval.

Hands-on experience working with one or more LLMs such as GPT, Claude, Gemini, or Llama.

Strong programming skills in Python and experience with Jupyter/iPython notebooks.

Good SQL skills.

Experience evaluating AI/ML systems, including accuracy testing, hallucination detection, grounding, and quality measurement.

Strong understanding of AI/ML system design, model selection, and real-world deployment considerations.

Excellent communication skills with the ability to work effectively with both technical and business stakeholders.

Ability to manage multiple projects, meet deadlines, and quickly learn new technologies and AI paradigms.

Must be a U.S. Citizen and able to obtain/maintain a Public Trust.

Preferred Qualifications

Experience with advanced prompt engineering techniques, including few-shot learning, zero-shot learning, and chain-of-thought prompting.

Experience with AWS or Azure cloud platforms and AI/ML services such as: AWS Bedrock, AWS SageMaker, Azure OpenAI, Azure AI Foundry, Amazon S3, AWS Lambda.

Experience with PyTorch, Keras, or other deep learning frameworks.

Experience with MLOps, model operationalization, deployment, and production monitoring.

Familiarity with Elasticsearch, Solr, vector databases, or other search/retrieval technologies.

Experience with Git and platforms such as Azure DevOps.

Familiarity with Linux and cloud CLI tools.

Experience creating interactive dashboards and data visualizations using Tableau, Power BI, or similar tools.

Experience with distributed NoSQL databases such as MongoDB or DynamoDB.

Experience building full-stack, scalable, and distributed systems optimized for performance.