Sr. MLOps Engineer

Job Description

Must have skills required :

Docker, Python, CI/CD, Kubernetes, MLOps

Good to have skills :

NoSql, SQL, Generative AI, Grafana, Prometheus

Inferenz (One of Uplers Clients) is Looking for:

Sr. MLOps Engineer who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you.

Role Overview Description

Building the machine learning production infrastructure (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization.

We are looking for a highly skilled MLOps Engineer to join our team. As an MLOps Engineer, you will be responsible for designing, implementing, and maintaining the infrastructure that supports the deployment, monitoring, and scaling of machine learning models in production. You will work closely with data scientists, software engineers, and DevOps teams to ensure seamless integration of machine learning models into our production systems.

The job is NOT for your if

You don't want to build a career in AI/ML. Becoming an expert in this technology and staying current will require significant self-motivation. You like the comfort and predictability of working on the same problem or code base for years. The tools, best practices, architectures, and problems are all going through rapid change -- you will be expected to learn new skills quickly and adapt.

Key Responsibilities:

Model Deployment: Design and implement scalable, reliable, and secure pipelines for deploying machine learning models to production.

Infrastructure Management: Develop and maintain infrastructure as code (IaC) for managing cloud resources, compute environments, and data storage.

Monitoring and Optimization: Implement monitoring tools to track the performance of models in production, identify issues, and optimize performance.

Collaboration: Work closely with data scientists to understand model requirements and ensure models are production ready.

Automation: Automate the end-to-end process of training, testing, deploying, and monitoring models.

Continuous Integration/Continuous Deployment (CI/CD): Develop and maintain CI/CD pipelines for machine learning projects.

Version Control: Implement model versioning to manage different iterations of machine learning models.

Security and Governance: Ensure that the deployed models and data pipelines are secure and comply with industry regulations.

Documentation: Create and maintain detailed documentation of all processes, tools, and infrastructure.

Qualifications:

- 5+ years of experience in a similar role (DevOps, DataOps, MLOps, etc.)
- Bachelor's or master's degree in computer science, Engineering, or a related field.
- Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
- Strong understanding of machine learning lifecycle, data pipelines, and model serving.
- Proficiency in programming languages such as Python, Shell scripting, and familiarity with ML frameworks (TensorFlow, PyTorch, etc.).
- Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
- Experience with CI/CD tools like Jenkins, GitLab CI, or similar
- Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
- Strong software engineering skills in complex, multi-language systems
- Comfort with Linux administration
- Experience working with cloud computing and database systems
- Experience building custom integrations between cloud-based systems using APIs
- Experience developing and maintaining ML systems built with open-source tools
- Experience developing with containers and Kubernetes in cloud computing environments
- Familiarity with one or more data-oriented workflow orchestration frameworks (MLFlow, KubeFlow, Airflow, Argo, etc.)
- Ability to translate business needs to technical requirements
- Strong understanding of software testing, benchmarking, and continuous integration
- Exposure to machine learning methodology and best practices
- Understanding of regulatory requirements for data privacy and model governance.

Preferred Skills:

- Excellent problem-solving skills and ability to troubleshoot complex production issues.
- Strong communication skills and ability to collaborate with cross-functional teams.
- Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack).
- Knowledge of database systems (SQL, NoSQL).
- Experience with Generative AI frameworks
- Preferred cloud-based or MLOps/DevOps certification (AWS, GCP, or Azure)

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