Values

The ad says“LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock).”
The ad says“Cloud-native experience with services like Amazon SageMaker, Google Vertex AI, or Azure ML”
The ad says“Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka.”
The ad says“Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).”
The ad says“Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).”
The ad says“Cloud-native experience with services like Amazon SageMaker, Google Vertex AI, or Azure ML”
The ad says“LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock).”
The ad says“Proficiency in GitOps practices and CI/CD pipeline development and management.”
The ad says“Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka.”
The ad says“API development with FastAPI or similar frameworks.”
The ad says“Proficiency in GitOps practices and CI/CD pipeline development and management.”
The ad says“Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).”
The ad says“LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock).”
The ad says“Observability and monitoring: Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems.”
The ad says“Java programming experience is a plus.”
The ad says“ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow.”
The ad says“LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar).”
The ad says“LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar).”
The ad says“LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar).”
The ad says“Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).”
The ad says“ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow.”
The ad says“Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).”
The ad says“Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).”
The ad says“Observability and monitoring: Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems.”
The ad says“Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn”
The ad says“RAG Systems: Experience building Retrieval-Augmented Generation systems and evaluating both retrieval quality and generation performance.”
The ad says“Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka.”
The ad says“Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn”
The ad says“Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn”
The ad says“Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).”
The ad says“ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow.”

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 clients 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

Work fully remote around the globe, or in a hybrid work model from our office hubs which are equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. Empower yourself with a stable workload and a stable income, focusing on lasting cooperation and high-performance culture.

Embrace the opportunity for personal and professional growth

Take advantage of performance review and career path. Elevate your skills with mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more.

Feel cared about

Be among like-minded people and feel supported with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values.

Interview steps

Interview 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)