Senior ML Operations Engineer

🔥 3 hours ago

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

SPD Technology

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2006

💳 Fintech

🤖 Artificial Intelligence

🤝 B2B

Fintech • Artificial Intelligence • B2B

SPD Technology is a global software product development company that delivers custom digital platforms, cloud and DevOps, AI/ML, and fintech/payment solutions. They specialize in application modernization, data analytics and engineering, system integration and APIs, security and quality engineering, and mobile/web UX/UI development. SPD works as a B2B partner for enterprise and growth-stage clients (notable customers cited include PitchBook and Poynt), focusing on building payment systems, billing and fraud-detection software, data platforms, and AI-enabled products for industries such as fintech, insurance, healthcare, and e-commerce.

📋 Description

• 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.

🎯 Requirements

• 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.