MLOps Specialist

Job Overview:

We are seeking an experienced MLOps professional to help deploy, scale and manage machine learning models in production environments. You will collaborate closely with data scientists and engineering teams to automate the machine learning lifecycle, optimize model performance and ensure seamless integration with data pipelines.

Key Responsibilities:

• Transform prototypes into production-grade models
• Support building and maintaining machine learning pipelines and infrastructure across cloud platforms such as AWS, Azure and GCP
• Develop REST APIs or FastAPI services for model serving, enabling real-time predictions and integration with other applications
• Collaborate with data scientists to design and develop drift detection and accuracy measurements for live models deployed
• Collaborate with technical teams to ensure compliance with engineering standards

Maintain Models in Production:

• Collaborate with data scientists and engineers to deploy, monitor, update and manage models in production
• Manage the full CI/CD cycle for live models, including testing and deployment
• Develop logging, alerting and mitigation strategies for handling model errors and optimizing performance
• Troubleshoot and resolve issues related to ML model deployment and performance
• Support both batch and real-time integrations for model inference, ensuring models are accessible through APIs or scheduled batch jobs, depending on use case

Contribute to AI Platform and Engineering Practices:

• Contribute to the development and maintenance of the AI infrastructure, ensuring the models are scalable, secure and optimized for performance
• Collaborate with the team to establish best practices for model deployment, version control, monitoring and continuous integration/continuous deployment (CI/CD)
• Drive the adoption of modern AI/ML engineering practices and help enhance the team's MLOps capabilities
• Develop and maintain Flask or FastAPI-based microservices for serving models and managing model APIs

Required Skills and Qualifications:

• Bachelor's degree in computer science, analytics, mathematics or statistics
• Strong experience in Python, SQL, Pyspark
• Solid understanding and knowledge of containerization technologies (Docker, Podman, Kubernetes)
• Proficient in CI/CD pipelines, model monitoring and MLOps platforms (e.g., AWS SageMaker, Azure ML, MLFlow)
• Proficiency in cloud platforms, specifically AWS, Azure and GCP
• Familiarity with ML frameworks such as TensorFlow, PyTorch, Scikit-learn
• Familiarity with batch processing integration for large-scale data pipelines
• Experience with serving models using FastAPI, Flask or similar frameworks for real-time inference
• Certifications in AWS, Azure or ML technologies are a plus
• Experience with Databricks is highly valued
• Strong problem-solving and analytical skills
• Ability to work in a team-oriented, collaborative environment

Tools and Technologies:

Model Development & Tracking: TensorFlow, PyTorch, scikit-learn, MLflow, Weights & Biases

Model Packaging & Serving: Docker, Kubernetes, FastAPI, Flask, ONNX, TorchScript

CI/CD & Pipelines: GitHub Actions, GitLab CI, Jenkins, ZenML, Kubeflow Pipelines, Metaflow

Infrastructure & Orchestration: Terraform, Ansible, Apache Airflow, Prefect

Cloud & Deployment: AWS, GCP, Azure, Serverless (Lambda, Cloud Functions)

Monitoring & Logging: Prometheus, Grafana, ELK Stack, WhyLabs, Evidently AI, Arize

Testing & Validation: Pytest, unittest, Pydantic, Great Expectations

Feature Store & Data Handling: Feast, Tecton, Hopsworks, Pandas, Spark, Dask

Message Brokers & Data Streams: Kafka, Redis Streams

Vector DB & LLM Integrations (optional): Pinecone, FAISS, Weaviate, LangChain, LlamaIndex, PromptLayer