Artificial Intelligence and Machine Learning Engineer

RIVA Solutions, Inc.

1d • $180,000 - $190,000

About The Position

RIVA Solutions seeks an AI Subject Matter Expert to support the USPTO P-TACTS program, the Patent Trial and Appeal Board (PTAB) case tracking and electronic filing system. The role designs, builds, and evaluates production AI solutions that convert unstructured legal filings into reliable structured data. A current example is a pipeline that reads PTAB petitions, institution decisions, and final written decisions and extracts trial-level and claim-level outcomes. The pipeline is measured against analyst-labeled ground truth, and its output supports reporting that PTAB delivers to Congress.

The engineer works mainly in Python on Databricks within a secured federal environment. Development takes place on remote instances through VPN, and LLM access is provided through an agency AI gateway; approved models may lag behind commercial offerings. The role works directly with government stakeholders and engineers to turn mission needs into working, maintainable, and measurable applications.

Requirements

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
• At least 5 years of applied ML, data science, or AI engineering experience, including at least 2 years building applications on large language models in production or near-production settings.
• Strong Python skills, including data work with pandas or PySpark.
• Solid machine learning fundamentals, including classification, train/test splits, overfitting, data leakage, and model selection.
• Hands-on evaluation skills, including precision, recall, F1, confusion matrices, class imbalance, and comparison against labeled ground truth.
• Production experience building with LLM APIs, including structured output, tool calling, and prompt design.
• Experience building or debugging an agent harness, and ability to explain when a structured pipeline is the better choice.
• Experience validating LLM output with schemas such as Pydantic or JSON Schema, retries, and exception handling.
• Daily use of AI coding assistants such as Claude Code, Codex, Cursor, or GitHub Copilot.
• Hands-on experience building and running ML or LLM workloads on Databricks or a major cloud platform (AWS, Azure, or GCP), such as PySpark pipelines or notebooks, hosted model services, and experiment tracking in MLflow.
• AWS Certified Machine Learning Engineer - Associate, an equivalent cloud ML certification, or an active AWS Certified Machine Learning - Specialty certification.
• Eligibility to obtain a USPTO Public Trust and authorization to work in the U.S.

Nice To Haves

• Databricks experience, including Jobs, Delta Lake, MLflow, and Unity Catalog.
• PyTorch experience, including fine-tuning small language models or training LoRA adapters.
• AWS experience, including Amazon Bedrock.
• Information extraction from scanned or long documents, including OCR and layout analysis.
• Experience with retrieval augmented generation and vector databases.
• MLOps experience, including pipeline orchestration, evaluation sets run in CI, and monitoring output quality over time.
• Experience working through an enterprise AI gateway with cost tracking and request filtering.
• Databricks Certified Machine Learning Professional, Databricks Certified Machine Learning Associate, Databricks Certified Generative AI Engineer Associate, AWS Certified Machine Learning Engineer - Associate, or an equivalent cloud certification.
• Experience with USPTO, PTAB, or patent and legal data.
• Prior delivery on federal government contracts, including FISMA Moderate systems.

Responsibilities

• Build LLM pipelines that extract structured data from long, unstructured legal documents.
• Determine whether tasks require a flexible agentic harness or a constrained, step-by-step pipeline, and build the appropriate solution.
• Validate model outputs against schemas, allowed values, and business rules, and handle failures without silently losing records.
• Design evaluations against ground truth, select appropriate metrics, perform error analysis, and report results in plain language.
• Use AI coding tools such as Claude Code daily to build, test, and review code.
• Keep pipelines stable as approved models change through version pinning, regression test sets, and prompt versioning.
• Advise the customer on future use cases, including fine-tuning small language models or adapters when supported by the data.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior