Senior Applied AI Engineer

Location: Hybrid from the Boston office 3 days per week; Boston, MA, United States. Potential remote work options are mentioned, but regular Boston office attendance is required.

Company

hirify.global develops verifiable and interpretable AI systems combining deep learning, formal logic, and physics-based modeling for semiconductor and photonic hardware design and simulation.

What you will do

• Own applied AI features end to end, from workflow discovery and technical design through implementation, rollout, and iteration.
• Design and implement LLM-powered product features, including tool-calling agents, structured output pipelines, retrieval and tool integrations, and safe prompting strategies.
• Select, evaluate, and optimize LLMs and agentic workflows based on quality, latency, cost, privacy, context limits, and reliability.
• Establish testing, evaluation, observability, security, and operational standards for applied AI development.
• Collaborate with users, stakeholders, backend engineers, AI developers, specialists, and scientists to integrate AI capabilities into the product.
• Review code, mentor junior AI developers, and define the workflow from notebooks and test repositories through pull requests, staging, and production.

Requirements

• 7+ years of software engineering experience, preferably with Python, including strong production ownership.
• Production experience with LLMs and AI/ML, including OpenAI API, HuggingFace, LangChain, or similar technologies beyond prototypes.
• Strong software engineering fundamentals, including design patterns, code structure, testing practices, code reviews, clear abstractions, API boundaries, typing, and error handling.
• Cloud infrastructure experience with GCP, preferably Vertex AI, or AWS with SageMaker.
• API and service development experience, such as FastAPI, REST, or asynchronous programming.
• CI/CD and DevOps experience with Docker, Terraform, and GitHub Actions.
• Experience with monitoring, observability, and instrumentation for debugging and iteration.
• Operating experience with AI deployment in an enterprise environment and experience shipping data projects to real users.
• Problem-solving ability, proactiveness, versatility, independent task definition and scoping, initiative, and the ability to clearly justify decisions and explain technical concepts.

Nice to have

• Experience fine-tuning or training models.
• Familiarity with LangChain, Pydantic AI, or similar frameworks.
• Knowledge of prompt engineering and evaluation techniques.
• Experience with real-time inference and streaming responses.
• Background in data engineering or ML engineering.
• Understanding of RAG architectures.
• Contributions to open-source AI/ML projects.

Culture & Benefits

• Competitive compensation and a stock options plan.
• Access to advanced tools and collaboration with experts in artificial intelligence, physics, hardware, and electronic design automation.
• Flexible work arrangements, including potential remote work options, within a hybrid Boston-office model.
• Opportunities to attend industry conferences, present research findings, and engage with the global AI research community.
• Work focused on AI architectures, formal consistency, scientific and engineering applications, and semiconductor and photonic hardware development.