[Hiring] Senior ML Operations @SPD Technology

Role Description

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.

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.

Qualifications

• 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.

Desired Skills

• Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).
• Proficiency in GitOps practices and CI/CD pipeline development and management.
• Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems.
• Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock).
• Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar).
• Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).
• 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.

Benefits

• Reveal great tech solutions by joining a team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling client growth.
• 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.
• 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.
• Work with a team of one mind who cares about what they do and how they do.
• Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values.

Interview Process

• Pre-Screening with the recruiter
• Tech Interview (ML Fundamentals, Software Engineering) + Live Coding (up to 1.5 hours)
• ML System Design Interview (up to 60 min)
• Interview with Engineering Manager (60 min)
• Final Client Interview (60 min)