AI Engineer

Position Overview

As an AI Engineer at Better Direct, you will own AI-powered features from concept through production—including the model, data pipeline, API, and user-facing experience. You will work across AI/ML systems and our application stack to turn complex documents and business workflows into reliable tools used by sales and engineering teams.

Key Responsibilities

• Build document-processing pipelines that transform RFQ PDFs, scanned pages, spreadsheets, and emails into structured data such as part numbers, quantities, manufacturers, and shipping addresses.
• Run, optimize, and monitor self-hosted LLMs, vision-language models, OCR systems, embedding models, and smaller task-specific models on on-premises NVIDIA GPUs and a Kubernetes-based AI cluster.
• Build search and RAG systems for contracts, product catalogs, and historical quotes using PostgreSQL, pgvector, BM25, vector retrieval, reranking, and deduplication.
• Develop tool-using agents that research products across distributor catalogs—including TD SYNNEX, Ingram Micro, and D&H—and prepare quotes for human review.
• Train and evaluate classifiers, NER models, sequence-labeling models, and other efficient alternatives when large language models are too slow or expensive.
• Create labeled evaluation datasets, regression tests, and performance benchmarks covering accuracy, recall, latency, reliability, and cost.
• Build and maintain Python services using FastAPI, asynchronous processing, Celery, and Redis for extraction, crawling, polling, and other background workloads.
• Ship AI capabilities into the CRM through Express and Prisma APIs, PostgreSQL schema changes and migrations, and React interfaces.
• Integrate AI output into production workflows using confidence scores, human review, feedback capture, permissions, and audit trails.
• Deploy and maintain services using Docker, Git, and documented runbooks. Monitor production jobs, diagnose failures, and support features after release.
• Write clear pull requests, design notes, test plans, and handoff documentation so other engineers can understand and maintain your work.

Required Qualifications

• U.S. citizenship.
• At least one year of experience building applications powered by LLMs or other machine-learning models that were used in real workflows.
• Strong Python skills, including experience with FastAPI or a similar framework, asynchronous programming, packaging, and automated testing.
• Working knowledge of TypeScript or JavaScript and experience building REST APIs or React applications, or the ability to learn these technologies quickly.
• Strong SQL and PostgreSQL fundamentals, including schema design, migrations, indexing, and query optimization.
• Hands-on experience in at least two of the following areas:
• RAG, embeddings, or semantic search
• Document extraction or OCR
• Agent tools, prompting, or workflow orchestration
• Model fine-tuning or training
• Model serving with vLLM, SGLang, or a similar platform
• Experience creating labeled test sets, selecting appropriate metrics, and objectively measuring whether a change improved performance.
• Comfort working with Linux, Git, and Docker.
• Clear written and verbal communication, including the ability to explain technical trade-offs to non-technical stakeholders.

AI-Assisted Engineering

AI-assisted development is part of how we work. Engineers may use tools such as OpenAI Codex to research unfamiliar systems, explore solutions, write code, generate tests, debug problems, and produce documentation.

These tools support engineering work, but responsibility for the final result remains with the engineer. You should be able to:

• Turn unclear business requests into well-defined technical tasks and acceptance criteria.
• Give AI tools the appropriate context, constraints, and technical requirements.
• Read, understand, explain, and maintain all code you submit.
• Recognize incomplete, unsafe, inaccurate, or unnecessarily complex generated solutions.
• Verify assumptions against the existing codebase, database, architecture, and application behavior.
• Evaluate trade-offs and select practical, maintainable solutions.
• Test permissions, security, data integrity, performance, failure modes, and edge cases.
• Use approved tools responsibly when working with company and customer data.
• Troubleshoot and support your work after it is released.

Preferred Qualifications

• Experience running open-weight models such as Qwen, Llama, or Mistral on self-managed GPUs.
• Knowledge of quantization, multi-GPU inference, model optimization, and NVIDIA driver or CUDA troubleshooting.
• Kubernetes experience, particularly with KServe or another model-serving platform.
• Experience using vision-language models or OCR frameworks such as Docling for document understanding.
• Background in traditional NLP techniques such as text classification, named-entity recognition, active learning, weak supervision, or conditional random fields.
• Experience with Celery and Redis, Selenium or Playwright, or S3-compatible storage such as MinIO.
• Familiarity with IT hardware and software products, manufacturer part numbers, and distributor catalogs.
• Contributions to open-source machine-learning or developer-tooling projects.

What We Value

• Systems thinking: Understanding how models, services, databases, interfaces, and integrations work together.
• Data correctness: Preventing duplicate, missing, inconsistent, or unauthorized changes.
• Practical problem-solving: Identifying root causes and comparing solutions based on evidence.
• Production ownership: Validating releases, monitoring results, and responding when problems occur.
• Scalability and maintainability: Recognizing designs that may become slow, fragile, or difficult to extend.
• Business understanding: Learning why a workflow exists, not simply how to implement it.
• Good judgment: Knowing when to proceed, investigate further, simplify the approach, or ask for help.
• Continuous learning: Developing new skills quickly and steadily taking on greater technical ownership.

We do not expect an AI Engineer to know every technology on the first day. We do expect curiosity, disciplined thinking, honest evaluation, and a willingness to take responsibility for production outcomes.

Compensation and Benefits

Salary range: $60,000–$70,000 per year, based on experience.

Better Direct is an equal opportunity employer under applicable law.

Pay: $60,000.00 - $70,000.00 per year

Benefits:

• 401(k)
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Professional development assistance
• Retirement plan
• Vision insurance

Work Location: In person