Senior AI Engineer

Total Comp

: \~$300k-$450k (flexible)

Hybrid:

1-2 days on-site per week

Key Responsibilities

• Develop and implement AI prototypes and pilots, including conversational agents, workflow automation, retrieval-augmented question answering (Q\&A), and intelligent copilots for internal teams
• Integrate large language models (LLMs), AI services, APIs, data warehouses, document stores, and productivity tools into enterprise systems
• Establish vector embedding pipelines and lightweight orchestration layers for pilot applications, including routing and context management
• Lead prompt engineering efforts, designing system prompts, maintaining prompt libraries, and optimizing prompt versions for reuse
• Conduct small-scale evaluations on prototypes focusing on quality, latency, hallucination rates, operational costs, and user engagement metrics
• Instrument prototypes to collect usage data and user feedback for iterative improvements
• Collaborate with data science, platform, and security teams to transition successful pilots into secure, supportable solutions
• Implement privacy and security controls aligned with internal policies (e.g., data redaction, role-based access, moderation)
• Embed safety guardrails within AI systems through model routing, input/output filtering, and content moderation mechanisms
• Document system architectures, data workflows, prompt strategies, known limitations, and risk factors
• Partner with AI enablement teams to train business users on AI workflows and copilots Core

Qualifications \& Requirements

• 4\+ years of practical experience in software engineering, machine learning engineering, applied data science, or related technical roles (startup or innovation lab experience preferred)
• Proven experience building with large language models (LLMs), including using commercial APIs, opensource models, RAG pipelines, vector databases, and orchestration frameworks
• Strong proficiency in Python and/or TypeScript/JavaScript, including API development, web interface creation, and scripting for automation
• Familiarity with modern data stack components such as data warehouses (e.g., Snowflake), transformation tools (e.g., dbt), and cloud services, with experience handling both structured and unstructured data
• Ability to interpret manual workflows, map processes to AI-enabled solutions, and implement prototypes rapidly in ambiguous problem spaces
• Knowledge of AI governance, security, and responsible AI principles, with capacity to incorporate these into system design •

Nice-to-Have Qualifications

• Experience in startup, skunkworks, or innovation lab environments
• Familiarity with enterprise AI deployment, model fine-tuning, and continuous integration/continuous deployment (CI/CD) practices
• Knowledge of regulatory compliance related to AI and data privacy