高级机器学习运维工程师
Senior ML Operations Engineer
我们公司
在SPD Technology,我们汇聚了一群志同道合的人,他们通过工作创造价值,致力于高绩效并提供定制化、前沿的技术解决方案,推动客户的增长。我们以卓越的文化赋能员工,并提供机会让他们在每个层面履行责任。我们重视人性与协作,鼓励专业和个人成长,营造一个支持性强、灵活的工作环境,欢迎每个人贡献自己的力量。
我们现在正在寻找一名高级机器学习运维工程师加入我们的团队。
职位描述
作为高级机器学习运维工程师,您将支持构建和部署人工智能驱动的解决方案的团队,帮助他们克服机器学习开发、部署和支持生命周期中的常见挑战。该职位提供自主决策权,鼓励积极主动地解决复杂问题,并为可扩展系统找到最佳解决方案。
项目介绍
PitchBook —— 为投资专业人士打造的平台。我们的软件提供数据访问和分析工具,快速获取答案并发现有前景的机会。揭示隐藏在超过三百万家公司财务数据中的可操作见解和趋势。全球用户包括大型企业、初创公司、风险投资和私募股权公司、投行等。PitchBook功能:高级搜索 / 发现与洞察 / 公司资料 / 工作流程与效率 / 财务信息等。
团队构成
1名工程经理,4名高级机器学习运维工程师
工作环境
该职位提供灵活的工作时间,您可以根据需要调整工作时间,但需参加所有团队会议。团队采用基于Scrum的敏捷方法。
作为一名合格的专家,您将
- 通过创建黄金路径来成为开发团队的倍增器,消除障碍并提升创意和创新。
- 在敏捷环境中与其他工程师、产品经理和内部利益相关者合作。
- 为团队成员提供指导、技术建议并进行代码审查。
- 在几乎没有指导的情况下,从头到尾设计并交付项目。
- 通过解决MLDLC中的常见痛点,为构建和部署AI应用的团队提供支持。
- 持续学习,并对发现新知识充满热情。
查看英文原文
About Us
At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed.
Now we are looking for a Senior ML Operations Engineer to join us as part of our team.
About the role
As a Senior ML Operations Engineer, you will support teams building and deploying AI-driven solutions, helping them overcome common challenges in the ML development, deployment, and support lifecycle. This role offers autonomy in decision-making, encouraging a proactive approach to solving complex issues and finding optimal solutions for scalable systems.
About the project
PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials and many more.
Team composition
1 Engineering Manager, 4 Senior ML Ops Engineers
Work Environment
The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology.
As a qualified expert, You will
- Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation.
- Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment.
- Provide mentorship, technical guidance, and perform code reviews for team members.
- Design and deliver on projects end-to-end with little to no guidance.
- Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC.
- Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities.
- Support the vision and values of the company through role modeling and encouraging desired behaviors.
- Participate in various cross-functional company initiatives and projects as requested.
- Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value.
- Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance.
We’re looking for you if you have
- Degree in Computer Science, Information Systems, Machine Learning, or a similar field preferred (or equivalent practical experience).
- 5+ years of hands-on software development experience with Python (Java experience with strong Python proficiency also considered).
- 4+ years of experience designing and building distributed software systems and architectures.
- 3+ years of hands-on experience deploying and operating Machine Learning services in production.
- Experience supporting ML lifecycle operations including post-deployment monitoring and maintenance.
- Experience in cloud-native stack, with a practical understanding of containerization technologies such as Kubernetes and Docker.
- Demonstrated experience with SQL and NoSQL database design and implementation.
- Ability to decompose complex problems into iterative, well-defined solutions.
- Strong problem-solving abilities with focus on building scalable, efficient, and maintainable systems.
- Strong communication and collaboration skills, with the ability to engage effectively with internal customers across various cultures and regions.
- Ability to be a team player who can also work independently.
- Experience working across multiple development teams is a plus.
Bonus points
- Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).
- Proficiency in GitOps practices and CI/CD pipeline development and management.
- Observability and monitoring: Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems.
- LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock).
- LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar).
- Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).
- RAG Systems: Experience building Retrieval-Augmented Generation systems and evaluating both retrieval quality and generation performance.
- Cloud-native experience with services like Amazon SageMaker, Google Vertex AI, or Azure ML
- Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn
- Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka.
- ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow.
- API development with FastAPI or similar frameworks.
- Java programming experience is a plus.
What’s in it for You
Reveal great tech solutions
Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling client growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients.
Experience an agile and flexible working environment
Enjoy the freedom of fully remote work with a flexible working schedule. Empower yourself with a stable workload and a stable income, supported by provided laptops and licensed software. We focus on lasting cooperation and unite result-oriented individuals who stand on a high-performance approach to work.
Embrace the opportunity for personal and professional growth
Benefit from performance and merit reviews, elevate your skills with personal development plans, and individual learnings through the corporate library, public speaking support, and more.
Be among like-minded people
Work with a team of one mind who cares about what they do and how they do. Collaborate with top-notch experts who are always ready to help and support you through any challenges. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. Feel supported by your HR, and take advantage of our referral bonus program.
Interview steps
- Pre-Screening with the recruiter
- Live Coding (up to 30 min)
- Tech Interview(ML Fundamentals, Software Engineering)(up to 60 min)
- ML System Design Interview (up to 45 min)
- Interview with Engineering Manager (60 min)
Originally posted on Himalayas