This role targets candidates searching for Senior AI Engineer, Machine Learning Engineer, NLP Engineer, or Applied Scientist roles with deep, hands-on experience shipping production information-extraction systems.

About Us

Our customer is a software development company delivering effective digital solutions to clients around the world, with a strong focus on the U.S. market. Headquartered in sunny San Diego, California, we've built a team of over 300 talented professionals across multiple countries. Fueled by courage and curiosity, we go beyond providing services; we dive deep into our clients' businesses to uncover real needs and tackle key challenges, combining human insight with technology. Our success comes from a deep commitment to results and long-term partnerships built on trust.

About the Role

As a Senior AI Engineer, you will design, train, and ship the models that turn messy, semi-structured business documents into reliable, structured data — for example extracting and decomposing the goods, parties, and terms from trade-finance documents and Letters of Credit. This is a hands-on, data-centric role: you own problems end to end, from pulling and cleaning production data, to training custom on-prem models and standing up LLM-based extractors, to proving they work with honest evaluation and deploying them into a live microservice platform.

You will set the technical approach for extraction workstreams, be relentless about data quality and evaluation rigor, and act as a trusted technical partner to the product and domain teams who depend on the results.

Key Responsibilities

• Build production extraction models for complex documents: token classification / sequence labeling, span segmentation and decomposition, and structured attribute extraction from semi-structured text (SWIFT/LC fields, invoices, packing lists, certificates).
• Fine-tune and train small, on-prem transformer models (encoder token-classifiers, small language models) on GPU, and stand up LLM-based extraction (Azure OpenAI / GPT) as baselines and PoCs — with anti-hallucination grounding and structured output.
• Own the data: pull from production stores (MongoDB), establish label provenance, deduplicate, and build leakage-free splits (e.g., by transaction/tenant) so metrics mean something.
• Build and maintain evaluation harnesses: report honest precision / recall / F1, make results reproducible, and actively hunt for contamination, overfitting, and mislabeled data before quoting a number.
• Make and defend trade-offs across accuracy, precision-vs-recall, cost, and latency, matched to how the model is actually used in production.
• Package models as containerized GPU/CPU microservices and ship them through GitOps (ArgoCD / Helm / Kapitan) across dev, QA, and production, with the right config and secrets handling.
• Define standards for evaluation, monitoring, and model lifecycle (MLOps / LLMOps), and build review tooling that lets domain experts validate and correct model output.
• Partner with product and domain experts to translate business rules and human-labeled data into robust, precision-first extraction; mentor engineers and set best practices.

Required Experience & Skills

• 5+ years of professional AI engineering, including production ML/NLP systems.
• Strong Python and applied deep learning with PyTorch and Hugging Face Transformers — real experience with token classification, sequence labeling, and fine-tuning encoder models.
• Hands-on model training on GPU (CUDA, mixed precision, long-context), and comfort operating remote training boxes (SSH / Teleport, Azure or GCP).
• Practical experience with LLM extraction pipelines (Azure OpenAI / OpenAI): structured output, grounding to source text, and prompt design used as a baseline, not the finish line.
• Data-centric ML: dataset construction from raw stores (MongoDB), deduplication, leakage-aware train/test splitting, and label-quality analysis.
• Evaluation rigor: metric design, honest reporting, and an instinct for when a number is too good to be true (leakage, memorization, sparse or synthetic labels).
• Deployment fundamentals: Docker, Kubernetes, and GitOps (ArgoCD / Helm / Kapitan a strong plus).
• Clear communication of results, trade-offs, and uncertainty to both engineers and non-technical stakeholders.

Nice to Have

• Domain exposure to trade finance, banking documents, or Letter-of-Credit processing.
• Document AI / OCR experience (hOCR, Azure Document Intelligence), table extraction, layout analysis, and document classification/routing.
• Serving/orchestration of ML services (durable queues, model facades) and cost/latency optimization for GPU inference.
• RAG / vector search where it genuinely fits — treated as one tool among many, not the default answer.

Leadership Expectations

• Set technical direction for extraction and model-training workstreams.
• Review and approve modeling approaches, experiment designs, and evaluation protocols.
• Act as the escalation point for hard ML and data-quality problems.
• Uphold a culture of honest, reproducible evaluation — no headline number ships without provenance.

Why Choose Us

• We’re a company where curiosity fuels everything - from bold ideas to personal growth.
• We believe common sense drives smart decisions. It keeps us focused and helps move fast, without the noise.
• We trust each other to deliver. Real ownership, no micromanagement.
• You’ll be heard here, even when you challenge the status quo. Because courage matters. Who dares — wins. Join us!