高级软件工程师 - 人工智能/机器学习
Senior Software Engineer - AI/ML
##### **在Mitratech，我们是一支技术型团队，专注于打造世界级的产品，简化法律、风险、合规和人力资源职能中的操作。我们是一个紧密协作、全球分布的团队，在一个支持个人卓越、以多样性和包容性工作文化为中心的生态系统中茁壮成长，注重优秀的人才实践、学习机会和乐趣！我们的文化是创业精神与企业投资的理想结合，使我们能够以快速的节奏，使用一些最复杂、最先进的技术进行工作。**
##### **在过去35年里，Mitratech的专家们一直专注于解决复杂需求。如今，我们为全球20,000家不同规模的客户公司提供服务，其中包括30%的财富500强企业，以及来自160多个国家的50多万用户。**
##### **随着我们不断发展壮大，我们始终在寻找有创造力、充满热情和新视角的人才。加入我们的全球团队，看看为什么Mitratech是一个真正出色的工作场所！**
鉴于我们持续的增长，我们始终有更多空间容纳智慧、活力和热情——加入我们的全球团队，了解为什么成为Mitratech的一员如此特别！
**职位概述**
我们正在寻找一位在生成式AI和大语言模型方面具有高度技能的高级软件工程师，重点在于代理系统、检索增强生成和AI评估，加入我们的动态团队。理想的候选人将在设计和交付符合复杂业务目标的生产级AI解决方案中发挥关键作用。该职位需要现代AI技术和软件工程方面的专业知识，同时对保持在技术前沿充满热情。
**核心职责与要求**：
- 设计、构建和运行多代理工作流和工具驱动的代理，实现编排逻辑、状态管理、安全防护措施和故障回退策略，确保生产管道的稳定性。
- 架构并维护端到端的RAG系统，涵盖文档摄入、分块、嵌入、向量检索、重新排序和答案合成，重点关注质量、归属和延迟。
- 评估并集成LLM和GenAI服务，从成本、性能和隐私维度进行选择，确定合适的托管模型和自建模型组合。
- 开发、版本化和优化提示策略；实现自动化提示测试和回归测试。
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##### **At Mitratech, we are a team of technocrats focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available.**
##### **For over 35 years, the experts at Mitratech have been focused on solving complex needs. Today, we serve 20,000 client companies of all sizes globally, representing 30% of the Fortune 500 and over 500,000 users in over 160 countries.**
##### **As we continue to grow, we’re always looking for resourceful, enthusiastic, and fresh perspectives. Join our global team and see what makes Mitratech a truly exceptional place to work!**
Given our continued growth, we always have room for more intellect, energy, and enthusiasm - join our global team and see why it's so special to be a part of Mitratech!
**Job Overview**
We are seeking a highly skilled Senior Software Engineer specialising in Generative AI and Large Language Models, with a strong focus on agentic systems, Retrieval-Augmented Generation, and AI evaluations, to join our dynamic team. The ideal candidate will play a pivotal role in architecting and delivering production-grade AI solutions that meet complex business objectives effectively. This position requires a blend of expertise in modern AI technologies and software engineering, along with a passion for staying at the forefront of advancements.
**Essential Duties & Responsibilities**:
- Design, build, and operate multi-agent workflows and tool-enabled agents, implementing orchestration logic, state management, safety guardrails, and fallback strategies for resilient production pipelines.
- Architect and maintain end-to-end RAG systems, covering document ingestion, chunking, embedding, vector retrieval, reranking, and answer synthesis with a focus on quality, attribution, and latency.
- Evaluate and integrate LLMs and GenAI services across cost, performance, and privacy dimensions, selecting the right mix of managed and in-house models.
- Develop, version, and optimise prompting strategies; implement automated prompt testing and regression tracking to maintain output quality and reliability.
- Define and own evaluation frameworks for generative outputs, including automated metrics, LLM-as-judge approaches, human evaluation protocols, hallucination detection, and drift monitoring.
- Apply classical NLP techniques where appropriate and maintain awareness of data distribution shifts that could impact model behaviour in production.
- Build and operate scalable, secure AI infrastructure on AWS (Bedrock, SageMaker, Lambda, OpenSearch), following well architected principles and infrastructure-as-code practices.
- Own the full deployment lifecycle: CI/CD for models and agents, testing strategies, observability, and rollback procedures.
- Ensure data quality through rigorous validation and augmentation, and proactively source datasets for training, fine-tuning, and evaluation.
**Requirements & Skills:**
- **Agent Orchestration:** Production experience designing multi-agent systems with tool use, memory/state management, and fault-tolerant routing. Familiarity with LangChain, LangGraph, AutoGen, or custom orchestrators.
- **RAG & Retrieval:** Hands-on experience building RAG pipelines end-to-end: chunking, embedding models, vector databases, retrieval tuning, and answer synthesis at production scale.
- **Evaluations:** Strong experience defining and running evaluation pipelines for generative AI — automated scoring, human evaluation design, hallucination mitigation, and drift monitoring. LLM-as-judge patterns are a plus.
- **LLMs & GenAI:** Demonstrated experience with foundation models and GenAI providers (AWS Bedrock, OpenAI, Anthropic, Meta). Comfortable with fine-tuning, instruction tuning, and prompt engineering at scale.
- **Traditional NLP:** Solid grounding in classical NLP techniques (NER, text classification, intent detection, topic modelling) and good judgement on when to apply them alongside or instead of LLMs.
- **AWS Bedrock:** Hands-on experience with Amazon Bedrock: foundation model APIs, Bedrock Agents, Knowledge Bases, and Guardrails. Experience with Bedrock Model Evaluation is a plus.
- **AWS Ecosystem:** Proficiency with SageMaker, Lambda, ECS/EKS, S3, OpenSearch, IAM, CloudWatch, and VPC networking.
- **MLOps & CI/CD:** Familiarity with model registries, CI/CD for ML, feature stores, canary deployments, monitoring, and rollback.
- **IaC:** Experience with Terraform or AWS CDK for reproducible infrastructure provisioning.
- **Python:** Production-quality Python: packaging, testing (pytest), type hints, async programming, and clean ML pipeline abstractions.
- **ML Frameworks:** Familiarity with traditional ML frameworks and fine-tuning workflows.
- **Experiment Tracking:** Experience with Langfuse, Arize or Langsmith, or equivalent for tracking runs, metrics, and artefacts.
- Ability to translate ambiguous business goals into concrete technical solutions and communicate tradeoffs to non-technical stakeholders.
- Strong collaborative instincts — comfortable working across engineering, product, and data teams.
- A rigorous, evidence-driven mindset: you ship with confidence because you measure, test, and monitor thoroughly.
**Education:**
- A Master’s degree in Machine Learning, Computer Science with a preference for specialization in the NLP domain.
**_We are an equal-opportunity employer that values diversity at all levels. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, disability, or veteran status._**