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Senior AI/ML Engineer
Not specifiedFull-timeAI & Machine LearningLegal Technology
Join a growing legal technology company as a Senior AI/ML Engineer. Build reliable, production-grade LLM and agentic systems that transform large volumes of unstructured information into structured signals and support legal risk products.
Responsibilities
- Design, build, deploy, and operate production AI systems, including agentic LLM extraction workflows and batch or online inference pipelines.
- Own AI solutions across exploration, deployment, monitoring, and iteration.
- Define quality metrics and build evaluation datasets, testing processes, and feedback loops for ML and LLM systems.
- Monitor accuracy, latency, cost, and data quality, and investigate and resolve system failures.
- Explore complex datasets to identify useful features and patterns and turn findings into production implementations.
- Partner with data engineers on ingestion, transformation, orchestration, and storage for dependable data products.
- Use AI-assisted coding tools effectively while maintaining technical judgment and system ownership.
- Help establish standards for testing, observability, reproducibility, data lineage, versioning, and safe AI deployment.
Requirements
- At least 6 years of experience in ML engineering, AI engineering, data science, or backend engineering for data-intensive systems.
- Hands-on experience taking AI or data systems from exploration through production, including deployment, monitoring, evaluation, versioning, and improvement.
- Strong Python skills and experience with SQL, NoSQL, data pipelines, and structured and unstructured data.
- Knowledge of feature engineering, statistical reasoning, experiment design, error analysis, and metrics such as precision, recall, accuracy, and coverage.
- Experience with LLM applications, including structured extraction, tool calling, agentic workflows, prompt design, embeddings, retrieval, and model evaluation.
- Understanding of production AI trade-offs involving quality, latency, scalability, reliability, and cost.
- Experience integrating AI into batch, orchestration, API, or event-driven workflows.
- Ability to work independently in ambiguous environments and drive solutions to production.
- Strong communication skills across engineering, data science, product, and domain-expert teams.
Nice to have
- Experience with document intelligence, information extraction, entity resolution, classification, ranking, or large-scale enrichment pipelines.
- Production experience with agentic systems.
- Natural language processing experience.
- Familiarity with Airflow, Dagster, Prefect, MLflow, or similar orchestration and machine learning tools.
- Experience with data warehouses, data lakes, vector databases, or modern data-processing frameworks.
- Cloud-native experience with AWS, Docker, Kubernetes, infrastructure as code, or CI/CD.
- Experience with human-in-the-loop review or feedback-driven AI systems.
- Familiarity with legal, financial, compliance, or other data-intensive domains where quality and explainability are important.