Solutions Engineer Jobs

About The Position

Requirements

• At least 7 years of experience in ML engineering, applied data science, or AI engineering.
• A track record of shipping ML or AI systems to production and owning them after launch, with the ability to explain architecture, tradeoffs, and success metrics.
• Hands-on experience training and evaluating models, including feature engineering, experiment design, metric selection, handling class imbalance and noisy labels, and judging when a model is ready to ship.
• Hands-on experience building production applications with LLMs, including RAG, embeddings, and vector databases.
• Working knowledge of MLOps practices and tooling such as MLflow, SageMaker Pipelines, Weights & Biases, or similar.
• Experience using AWS for ML workloads, such as SageMaker, Bedrock, Lambda, ECS/EKS, or S3.
• Strong Python skills and sound engineering practices, including version control, testing, CI/CD, and code review.
• A habit of building quickly to learn and testing ideas rather than debating them at length.
• Daily use of AI coding assistants such as Cursor, Claude Code, GitHub Copilot, or similar, and curiosity about new AI tools.

Nice To Haves

• Experience with LLM fine-tuning and the ability to choose when to fine-tune, prompt engineer, or train a smaller task-specific model.
• Experience reducing inference costs at high volume through distillation, quantization, caching, or tiered model routing.
• Background in retail, e-commerce, or product data, including catalog matching, taxonomy mapping, or attribute extraction.
• Experience with entity resolution or similarity search at scale.
• Comfort working with data pipelines and orchestration tools such as Airflow; helpful but not required.

Responsibilities

• Rapidly turn customer problems into working prototypes, use data to validate or reject approaches, and move successful solutions into production.
• Train, fine-tune, and evaluate models for matching, classification, and extraction, including classical machine learning, embedding models, and LLMs; select an approach that meets quality and cost requirements.
• Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS.
• Own the ML lifecycle, including experiment tracking, model versioning, deployment, monitoring, and retraining.
• Build evaluation methods, including labeled golden sets, offline precision and recall metrics, LLM-as-judge where appropriate, and shadow or A/B testing before rollout.
• Monitor production models for drift, output quality, latency, and cost per prediction, and address issues found.

Benefits

• Performance-based discretionary bonuses and variable pay plans are available for some positions.