AI/ML Engineer
在哈里斯，你将作为5个不同业务垂直部门的一部分工作，公用部门，医疗保健，公共事业，保险和私营部门，拥有超过12,000名员工和全球200个国家的100,000多家客户。我们需要你的帮助继续增长，并希望你可以成为哈利斯家族的一个组成部分。我们正在寻找一个 AI/ML 工程师来帮助我们在生产中构建，部署和运行 AI 和机器学习系统。你将专注于工程强度和可扩展性，桥梁数据科学和软件工程以确保模型在现实应用程序中可靠地执行：主要功能：· 建立和部署人工智能解决方案在生产系统中 · 使用 API,微服务或批量管道进行模型的生产 · 实施基于 LLM 的系统 （RAG,嵌入式，评估，快速优化）· 优化 AI 服务的性能，延迟，成本和可靠性 · 维护模型监测，记录和重训工作流程 · 与数据工程师合作以确保数据质量和可用性 · 遵循既定的 MLOP,DevOps 和云标准 · 处理问题模式，数据和基础设施问题的职位资格：我们正在寻找的资格是工作经验和教育背景的混合。专业人员分为最低资格 （必须有）和额外资格 （好用）,以及软技能 （能力）: · 4年以上经验大型语言模型 (LLM) 和自然语言处理 (NLP) 使用：LangChain,LangGraph,LlamaIndex · 3年以上支持和开发 API/微服务与 Java, .Net 或 JavaScript. · 3年以上经验使用生成人工智能工具 （例如 OpenAIs GPT,Anthropic Claude,Google Gemini）. · 3年以上工作经验作为 Python 的开发者。· 3 年经验在机器学习框架 （如 TensorFlow, PyTorch+）上。· 3年以上与关系数据库 (SQL) 工作经验额外资格：· AI 认证 · ML 认证 ·云认证 (AWS,Azure) 软技能：· 在协作和凝聚的团队环境中有效工作的记录证明 ·卓越的客户服务和组织能力 · 特殊的分析，故障解决和解决问题的技能上述声明旨在描述一般性和水平。
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Here at Harris, you’ll be working as part of 5 different business verticals, Public Sector, Healthcare, Utilities, Insurance and Private sector, with over 12,000 employees and more than 100,000 customers located in 200 countries around the globe. We need your help to keep growing and we hope you can become an integral part of the Harris family.
We are looking for an AI/ML engineer to help us build, deploy, and operate AI and machine-learning systems in production. You’ll focus on engineering robustness and scalability, bridging data science and software engineering to ensure models perform reliably in real-world applications.
Primary Functions:
- Build and deploy ML and Generative AI solutions into production systems
- Productionize models using APIs, microservices, or batch pipelines
- Implement LLM-based systems (RAG, embeddings, evaluation, prompt optimization)
- Optimize performance, latency, cost, and reliability of AI services
- Maintain model monitoring, logging, and retraining workflows
- Work with data engineers to ensure data quality and availability
- Follow established MLOps, DevOps, and cloud standards
- Troubleshoot model, data, and infrastructure issues
Job Qualifications:
The qualifications we are looking for are mixture of work experience and educational background.
They are split into Minimum Qualifications (must have) and Additional Qualifications (nice to have) along with soft skills (competencies) needed for the role:
Minimum Qualifications:
- 4+ years of experience with large language models (LLMs) and natural language processing (NLP) using: LangChain, LangGraph, LlamaIndex
- 3+ years of experience supporting and developing API/Microservices with Java, .Net or JavaScript.
- 3+ years of experience working with generative AI tools (e.g., OpenAI’s GPT, Anthropic Claude, Google Gemini).
- 3+ years of experience working as developer with Python.
- 3+ years of experience with machine learning frameworks (e.g., TensorFlow, PyTorch)
- 3+ years of experience working with relational databases (SQL)
Additional Qualifications:
- AI certifications
- ML certifications
- Cloud certifications (AWS, Azure)
Soft Skills:
- Demonstrated track record of working effectively within a collaborative and cohesive, team-based environment
- Outstanding customer service and organizational skills
- Exceptional analytical, troubleshooting, and problem-solving skills
The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. It is not designed to be utilized as a comprehensive list of all duties, responsibilities, and qualifications required of employees assigned to this job.
Working Environment:
This job operates in a professional office environment or remote home office location. This role routinely uses standard office equipment such as computers, phones, photocopiers, filing cabinets and fax machines. Periods of stress may occur.
This role may occasionally encounter Protected Health Information, Personal Identifiable Information or Privacy Records, and it is essential that all employees adhere to confidentiality requirements as outlined in the Employee Handbook and Harris’ Security and Privacy policies, as well as apply the concepts learned in the annual Security Awareness training.
Originally posted on Himalayas