Ai Engineer

Description

The company is a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization, and has operated for more than 140 years.

It serves customers in data centers, healthcare, pharmaceuticals, advanced manufacturing, and higher education, helping them use energy more efficiently and cut carbon emissions.

The AI Engineer role sits within the Data Science and Analytics team. It suits an engineer who has solid software, data, and ML skills, hands-on generative AI experience, and a data scientist's interest in how models behave. You will build the pipelines, tooling, and applications that make AI and LLM models reliable in production.

You will own defined AI projects end to end, from data pipeline to deployed application, making sound technical calls within your scope and guiding junior engineers on specific problems.

Responsibilities

• Build and deploy generative AI systems and LLM-powered applications, such as enterprise search, document summarization, and conversational AI, using models like GPT, Claude, and LLaMA.
• Adapt foundation models to domain-specific needs through prompt engineering, fine-tuning, and orchestration.
• Create agentic workflows and task-specific AI agents with Palantir AIP or the Microsoft Agent Framework that coordinate tools, retrieval, and reasoning.
• Assess and improve model outputs for accuracy, relevance, latency, and cost, using data science methods to measure results.
• Build and maintain ingestion, transformation, and ETL pipelines for structured and unstructured data, using sources such as Snowflake and Azure.
• Develop and run ML pipelines and MLOps workflows covering training, evaluation, deployment, and monitoring, with CI/CD, Docker, and model serving.
• Build reusable components, services, and APIs around AI models so the team can ship features faster.
• Implement retrieval and embedding workflows using RAG and vector databases for accurate, scalable knowledge retrieval.
• Apply testing, version control, and code review practices across your projects.
• Work with product, engineering, and business teams to turn business problems into AI solutions.
• Run workshops and proofs of concept that show the value of LLM and agent use cases across business units.
• Explain model results, data findings, and technical tradeoffs to non-technical audiences.
• Guide junior engineers on technical problems and code quality, contribute to design discussions, and share knowledge to raise engineering and data science standards.

Requirements

• A degree in computer science, software engineering, data engineering, data science, or a related technical or quantitative field.
• 2-5 years of experience in software, data, ML engineering, or data science, including hands-on work with LLMs or generative AI.
• A record of delivering data or ML pipelines and AI or ML solutions into production.
• Experience with data science basics such as exploratory analysis, statistical modeling, or classic ML like classification, regression, and forecasting.
• Experience with a cloud AI platform such as Azure OpenAI or Azure ML, AWS SageMaker or Bedrock, or Google Cloud Vertex AI.
• Strong Python and SQL skills, with good engineering habits including testing, version control, and clean code.
• Hands-on work with the generative AI stack: prompt engineering, fine-tuning such as LoRA, LLM orchestration, and agent frameworks like LangChain, Semantic Kernel, or Microsoft Agent Framework.
• Experience building ETL and ML pipelines and applying MLOps practices such as CI/CD, Docker, and model serving.
• Familiarity with pandas, scikit-learn, and model evaluation and experimentation workflows.
• Experience with the company's stack or comparable platforms, such as Palantir AIP, Azure ML, Microsoft Agent Framework, Power Automate, and Snowflake.
• Working knowledge of embeddings, vector databases, and retrieval systems.
• Ability to own projects and clearly communicate progress, risks, and tradeoffs.
• Strong collaboration skills across product, engineering, and business teams.
• Comfort presenting technical and analytical work to both technical and non-technical audiences.
• Self-directed, able to manage priorities independently and solve problems without close supervision.
• Preferred: experience with IoT, edge analytics, or smart building systems.
• Preferred: familiarity with LLMOps, LangChain, Semantic Kernel, or similar orchestration frameworks.
• Preferred: data science depth in statistical modeling, experimentation, or deep learning for forecasting, computer vision, or NLP.
• Preferred: experience with the Microsoft ecosystem, including Microsoft 365 Copilot, SharePoint, Power Platform, and Snowflake.
• Preferred: knowledge of data privacy and governance for enterprise LLM use.

Conditions

The company will not sponsor applicants for work visas or provide immigration-related employment sponsorship for this position, now or in the future.

The target salary range is $85,000 to $110,000. Pay depends on education, experience, knowledge, skills, and abilities, internal equity, location, and market data.

Candidates whose background goes beyond the stated range are encouraged to apply.

Technology-assisted tools, including AI, may help screen and evaluate applicants, but human reviewers make all hiring decisions.

The position includes a competitive benefits package.

Benefits

• Competitive salary.
• Paid vacation, holidays, and sick time.
• Comprehensive benefits including a 401K plan and medical, dental, and vision care.
• On-the-job and cross-training opportunities.
• A supportive, collaborative team.
• A safety commitment under the company's Zero Harm policy.