MlOps Lead Engineer - Only on W2 (EST Time Zone)

Job Title: MlOps Lead Engineer

Location: 100% Remote out of EST

Duration: 12+ Months

Note: 100% Remote out of EST

Top Skills' Details

1. 3+ years of experience operationalizing AI/ML models within an Azure environment with 10 year of total engineering experience
   2. Strong Python and Docker/Kubernetes experience
   3. 2+ Experience with Azure AI/ML services

ML Ops engineer to create data platform and pipeline to enable advanced analytics.
ML OPS Engineers are the crafters of automation, turning data-driven models into practical applications.
They take the prototypes developed by Data Scientists and fine-tune them for scalability, efficiency, and real world deployment. Their expertise in machine learning frameworks and software engineering ensures that the
predictive power of models seamlessly integrates into everyday operations.

ESSENTIAL DUTIES:
Utilize Azure technologies like Azure Cognitive Technologies, Azure Machine Learning, and Azure Bot
Services to design, create, and deploy AI/ML based applications.
Include AI components into data workflows, engage with data scientists and data engineers.
Utilize Azure AI services to implement natural language processing (NLP) create and implement
machine learning models and algorithms.
Automate the deployment and monitoring of AI models, collaborate with DevOps teams.
Use AI to automate processes such as sentiment analysis, image identification, recommendation
systems, and chatbots.
Implementing machine learning pipelines and workflows
Deploying and scaling ML models in production environments
Automating CI/CD pipelines to account for data, code, and model changes
Monitoring model performance and applying updates as needed
Ensuring the security and compliance of machine learning systems
Collaborating with data scientists to optimize models and improve performance

POSITION REQUIREMENTS & COMPETENCIES:
Bachelor s Degree, (BA/BS) in Information Systems from a four-year college or university and 5 or more
years of development experience required or equivalent combination or education and experience
Travel up to 25%
Total of 3-6 years of experience in managing machine learning projects end-to-end, with the last 18
months focused on ML Ops
Strong programming skills, preferably in languages like Python, Java, or Scala
Proficiency in machine learning libraries and frameworks, such as TensorFlow, PyTorch, or scikit-learn
Experience with containerization technologies, like Docker and Kubernetes
Familiarity with ML model deployment tools, such as MLflow or Kubeflow
Working experience in Azure cloud platform