Senior MLOps

Your Typical Day With Us

As a Senior MLOps Engineer, you will be at the forefront of deploying, optimizing, and monitoring LLMs in production environments. Your role will involve building and maintaining scalable pipelines, ensuring low-latency inference, and implementing best practices in monitoring and observability. You will also work with state-of-the-art tools like Hugging Face and MLFlow to fine-tune models and integrate them into robust AI solutions.

Key Responsibilities

• Model Deployment & Management
• Develop and maintain scalable pipelines for deploying LLMs, focusing on efficient, low-latency inference.
• Utilize tools like Hugging Face and MLFlow for seamless model integration and version control.
• Automate deployment processes, including model validation and continuous integration.
• Monitoring & Observability:
• Implement comprehensive monitoring frameworks to track performance and reliability of models in production.
• Use advanced observability tools to proactively detect and address performance issues.
• Deploy alerting systems to ensure rapid response to anomalies in model behavior.
• Infrastructure Optimization:
• Architect and optimize cloud and on-premise infrastructure to support large-scale LLM operations.
• Collaborate with cloud providers like AWS, Azure, and GCP to optimize costs and performance.
• Work with backend engineers to ensure smooth integration of AI models into conversational platforms.
• Collaboration & Documentation:
• Partner with AI engineers and data scientists to align on project objectives and deployment strategies.
• Document MLOps processes, best practices, and tools to maintain operational excellence.
• Provide training and support to team members on MLOps methodologies and tools.

What You’ll Need

• Experience
• 5+ years of experience in MLOps, DevOps, or related fields, with a focus on deploying and managing LLMs or other large-scale machine learning models.
• Proven experience with tools like Hugging Face, MLFlow, and containerization technologies (Docker, Kubernetes).
• Strong experience with cloud platforms (AWS, Azure, GCP) and infrastructure as code (Terraform).
• Hands-on experience in reducing inference latency and optimizing AI infrastructure.
• Technical Skills:
• Proficiency in Python, with experience in ML libraries such as TensorFlow, PyTorch, and related frameworks.
• Expertise in CI/CD pipelines, version control (Git), and orchestration tools.
• Familiarity with Generative AI, prompt engineering, and deploying models at scale.
• Soft Skills:
• Excellent problem-solving skills with the ability to tackle complex challenges independently.
• Strong communication skills, with the ability to translate technical concepts for non-technical stakeholders.
• A proactive mindset with a focus on continuous learning and staying updated with industry trends.

Don’t just take our word for it  

Zeals has received the LinkedIn Top Startups award twice in a row. We believe this is a testament to our growth and our ability to attract great talents–who could potentially be your colleague! 

Our Tech Stack

• Backend Languages: Go, Python, Elixir
• Frontend Languages: HTML, CSS, JavaScript (TypeScript, React.js, Recoil, Zod, Tanstack, etc)
• Mobile Languages: None
• Infrastructure: Google Cloud Platform, GCP Pub/Sub, Kubernetes, MongoDB, MySQL, Postgres, BigQuery, Elasticsearch, Qdrant
• Configuration management tool: Terraform
• CI/CD: Github Actions, ArgoCD, CircleCI
• Monitoring: Grafana, GCP Cloud Monitoring, GCP Logging, GCP Cloud Trace, Opsgenie
• Data: BigQuery, Parquet, Spark, Scala, Python, dbt
• Knowledge Tools: Confluence
• Other: GitHub / Slack / Jira
• Process: Scrum

Ready to roll up our sleeves