Top 10 MLOps Jobs Aspirants Should Apply for in July 2022



MLOps

With the growing importance of MLOps here are the top 10 MLOps jobs that you can apply for in July

MLOps platforms are increasingly being applied to ML models and the teams that build them to optimize and standardize the procedures that go into model lifecycle management. MLOPs have evolved as an independent approach in machine learning, which applies to the entire life cycle from data gathering to model deployment. MLOPs enable the channels of communication between data scientists and operations professionals. MLOps offers exciting opportunities for advancement and standardizing best practices in machine learning development. MLOps is a set of practices that teams of data scientists and IT engineers obey to increase the speed of machine learning models’ deployment in real-world projects. This article lists the top 10 MLOps jobs to apply for in July 2022.

MLOps / Sr MLOps Engineer
Tredence

Responsibilities:

  • Engage with clients to understand current and future business goals and translate business problems into a high-level solution approach
  • Work hands-on on building the model deployment and monitoring pipelines based on an in-depth understanding of underlying data, data structures, and business problems to ensure deliverables meet client needs
  • Effectively communicate the approach and insights to a larger business audience
  • Collaborate with team members, peers, and leadership at Tredence and client companies

Requirements:

  • At least 2 years of work experience
  • Bachelors or Masters’s degree in a CS or other engineering discipline with solid programming knowledge
  • Hands-on Python coding (intermediate to advanced level)
  • Knowledge of ML modeling, lifecycle
  • Excellent communication skills

Click here to apply

MLOps Engineer
NVIDIA

Responsibilities:

  • Maintain dataset preparation & training workflows for conversational AI models
  • Expand and maintain the Data lake
  • Ensure traceability and versioning of datasets, models & evaluation pipelines
  • Maintain & enhance dashboards for visualization of datasets
  • Collaborate with various teams on MLOPs infra & workflow enhancements

Requirements:

  • Bachelor’s degree or Master’s degree (or equivalent experience) or Ph.D. in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math
  • 3+ years of Experience
  • Understanding of MLOPS life cycle & experience with MLOPS workflows & traceability and versioning of datasets
  • Know-how of database management and queries (in SQL etc)
  • Familiarity with ETL data engineering and UI/UX full-stack development

Click here to apply

Senior MLOps Engineer
NorthHill Technology Resources

Responsibilities:

  • Establish scalable, efficient, and automated processes for ML real-time and batch deployments
  • Design, build and maintain the ML CI/CD pipeline that automates data preparation, data analysis, experimentation, model training, model serving, monitoring, and visualizations in production.
  • Work closely with data scientists, data engineers, and software engineers to develop processes, identify bottlenecks, and create effective solutions.

Requirements:

  • Working knowledge of ML concepts, including data preparation and visualization, with experience in ML model development (including training and prediction), deployment, and management
  • Comprehensive understanding of computer programming, including python
  • Excellent analytical, problem-solving, and communication and collaboration skills
  • A minimum of 10 years of experience in the Information Technology field focusing on AI/ML engineering projects, DevSecOps, and technical architecture.

Click here to apply

MLOps Engineer

Capgemini

Responsibilities:

  • Develops the strategies, blueprints, and processes for MLOps to be used while identifying any risks inherent in the life cycle
  • Design and build effective, user-friendly infrastructure, tooling, and automation to accelerate Machine Learning at Hyperscale
  • Collaborate with teams to drive the ML technical roadmap
  • Collaborate with Machine Learning Engineers and Product Managers to develop tools to support experimentation, training, and production operations
  • Manage & Oversee MLOps life cycle and processes and unify the work of data scientists, data engineers, and software developers

Click here to apply

DevOps/MLOPs Engineer

UST

Requirements:

  • Should have experience in Developing end-to-end ML pipelines
  • Must have experience in Setup CI/CD/CT pipelines for ML algorithms
  • Good to be strong in Cloud Platforms, especially GCP and Azure
  • Should have work experience in related skills – Docker, Kubernetes, edge computing
  • Proactive and good in communication
  • Should have experience in expertise in any of the following Task orchestration Tools – MLflow, Kubeflow, Airflow, Vertex AI, Azure ML, etc

Click here to apply

AI Engineering / MLOPS

LTI – Larsen & Toubro Infotech

Responsibilities:

  • As a Senior ML Engineer, you will be a key stakeholder and own responsibility in designing and architecting end-to-end ML solutions
  • Operationalize and monitor machine learning models using high-end tools and technologies.
  • Design & implementation of DevOps principles in Machine Learning
  • Data Science quality assurance and testing
  • Model Governance and Monitoring
  • Execute best practices in version control and continuous integration/delivery

Requirements:

  • Minimum 5+ years of experience in AI/ML
  • Experience in implementing machine learning life cycle on AWS (Using Sage Maker) or Azure (Azure ML) or GCP other cloud platforms
  • Experience with Docker, Jenkins, Kubernetes, and other DevOps tools.
  • Good Programming skills (at least one of Python/Spark/R).
  • Experience with Machine learning frameworks, libraries, and agile environments.
  • Experience with version control tools such as Git, Bitbucket, etc.

Click here to apply

Data Engineer – ML Ops

Visa

Responsibilities:

  • Work closely with Data Scientists to determine and refine machine learning objectives.
  • Ensure that algorithms generate recommendations as expected by testing and training the ML models by developing approaches/functions to analyze huge volumes of historical data.
  • Build MLOps pipelines to support development, experimentation, continuous integration, continuous delivery, verification/ validation, and monitoring of AI/ML models
  • Run tests, perform statistical analysis, and interpret test results executing the ML models

Requirements:

  • Minimum of 4+ years of analytics expertise in building Data and ML pipelines to the models built by the Data Scientist community
  • 4+ yrs. work experience with a Bachelor’s Degree or 3+ years of work experience with a Master’s or Advanced Degree with specialization in Computer science, Information science, Statistics, Data Engineering, and Analytics or a relevant area
  • Good understanding of the Payments and Banking Industry including aspects such as consumer credit, consumer debit, prepaid, small business, commercial, co-branded, and merchant

Click here to apply

AI Engineering / MLOPS
LTI – Larsen & Toubro Infotech

Responsibilities:

  • As a Senior ML Engineer, you will be a key stakeholder and own responsibility in designing and architecting end-to-end ML solutions
  • Operationalize and monitor machine learning models using high-end tools and technologies.
  • Design & implementation of DevOps principles in Machine Learning
  • Data Science quality assurance and testing
  • Model Governance and Monitoring

Requirements:

  • Minimum 5+ years of experience in AI/ML
  • Experience in implementing machine learning life cycle on AWS (Using Sage Maker) or Azure (Azure ML) or GCP other cloud platforms
  • Experience with Docker, Jenkins, Kubernetes, and other DevOps tools
  • Good Programming skills (at least one of Python/Spark/R)
  • Experience with Machine learning frameworks, libraries, and agile environments
  • Experience with version control tools such as Git, Bitbucket, etc.

Click here to apply

MLOps Engineer
Siemens Healthineers

Responsibilities:

  • Design, Build and maintain the ML Ops platform which includes operationalizing and orchestrating machine learning models, their inputs and outputs
  • Transform AI/ML code into production code via infrastructure that can perform at scale and are resilient
  • Design, deploy and maintain the full ML platform stack including capabilities such as monitoring & full model life-cycle
  • Engage with data engineers, data scientists, and others to set up ML pipelines
  • Create and mature CI/CD for machine learning and AI development

Requirements

  • 3+ experience as ML Ops/ Data Science/ Machine learning Engineer
  • Expert in building, testing, monitoring, and maintaining robust large-scale machine learning pipelines designed for scalability, automation, and CI/CD
  • Good knowledge of at least one machine learning framework such as TensorFlow, PyTorch, Scikit-Learn

Click here to apply

MLOps Engineer

Siemens

Responsibilities:

  • Design, Build and maintain the ML Ops platform which includes operationalizing and orchestrating machine learning models, their inputs and outputs
  • Transform AI/ML code into production code via infrastructure that can perform at scale and are resilient
  • Design, deploy and maintain the full ML platform stack including capabilities such as monitoring & full model life-cycle

Requirements:

  • 3+ experience as ML Ops/ Data Science/ Machine learning Engineer
  • Expert in building, testing, monitoring, and maintaining robust large-scale machine learning pipelines designed for scalability, automation, and CI/CD
  • Good knowledge of at least one machine learning framework such as TensorFlow, PyTorch, Scikit-Learn
  • Strong fundamentals of Cloud (Microsoft Azure Preferred) and DevOps

Click here to apply

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