Senior AI Engineer

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What the market looks like

The market for specialist-level AI engineering talent remains active across the US, with 37,300+ postings in the last six months concentrated in California, New York, and Texas. Demand spans Professional Services, Technology, Financial Services, and Healthcare sectors. Compensation for this cohort typically ranges from $170K to $340K annually. The strongest candidates bring production experience shipping AI systems end-to-end—from model selection and training through deployment and monitoring—combined with software engineering discipline, practical knowledge of LLM tooling, and the ability to communicate complex technical decisions to non-technical stakeholders.

Job responsibilities

• Design, build, and ship AI/ML systems and features in production environments, owning quality and performance across the full lifecycle

• Select and evaluate ML frameworks, model architectures, and deployment strategies based on business requirements and technical constraints

• Partner with data scientists and product teams to translate research and prototypes into reliable, scalable systems

• Implement monitoring, logging, and observability to track model performance, detect drift, and respond to production issues

• Integrate large language models, embeddings, and other AI/ML components into application workflows, managing latency and cost trade-offs

• Lead code reviews and mentor junior engineers, establishing best practices for ML code quality and testing

• Collaborate with infrastructure and platform teams to optimize compute resources and ensure reproducible, auditable AI pipelines

Candidate requirements

• 5+ years of professional software engineering experience, with at least 2–3 years actively building and deploying ML/AI systems in production

• Strong fundamentals in Python, software architecture, and debugging; hands-on experience with ML frameworks (PyTorch, TensorFlow, or equivalent) and MLOps tooling

• Demonstrated ability to own a system end-to-end: from problem definition through model selection, training, evaluation, and monitoring

• Experience working with LLMs, retrieval-augmented generation (RAG), or fine-tuning in a production context

• Comfort explaining technical trade-offs and AI system behavior to product, business, and non-technical audiences

• Track record of shipping features or systems on schedule and collaborating effectively across teams in fast-moving environments