Senior ML Ops Engineer

2 months ago

Ahmedabad, India Infraveo Full time

This is a remote position.

Were looking for an AI/MLEngineer with a minimum of 6 years of experience. Your experienceand skills in Python SQL ML & Ops Engineering and MLOpsusing AWS Cloud and Kubernetes along with your ability to lead andinnovate in deploying ML/AI pipelines reflect a strong foundationin the field. Your focus on scalable architecture collaborationacross teams and continuous learning in ML research positions youwell to tackle complex business problems and deliver impactfulsolutions.

Responsibilities:

  • Design build and maintain scalableML infrastructure using cloud platforms like AWS Azure or GoogleCloud ensuring efficient model deployment andmanagement.
  • Develop and implement CI/CD pipelines usingtools like Jenkins GitLab CI/CD or CircleCI to automate theintegration and deployment of machine learningmodels.
  • Containerize ML applications using Docker andorchestrate deployments with Kubernetes (K8s) to ensure scalabilityreliability and seamlessupdates.
  • Collaborate with data scientists dataengineers and software engineers to integrate ML models intoproduction systems ensuring alignment with businessobjectives.
  • Monitor and maintain the performance of MLmodels in production using monitoring tools like Prometheus Grafanaor ELK stack and implement alerts for model drift or performancedegradation.
  • Manage the versioning and lifecycle of MLmodels using tools like MLflow DVC or TFX ensuring reproducibilitytraceability andcompliance.
  • Optimize ML pipelines for performance andcostefficiency employing techniques such as distributed computingdata parallelism and automated hyper parametertuning.
  • Ensure the security and compliance of MLmodels and data pipelines by implementing best practices in dataencryption access controls and auditlogging.
  • Conduct experiments and A/B tests to evaluatemodel performance and iterate on models based on feedback evolvingrequirements and newdata.
  • Provide technical leadership and mentorshipto a team of ML engineers fostering a collaborative environment anddriving continuous improvement in MLOpspractices.


Requirements
  • Ability to adapt tonew technologies tools and methods in the rapidly evolving MLOpslandscape.
  • Constant drive to innovate andapply the latest ML research to improve processes and solve complexproblems.
  • Proficiency in Python and SQL forscripting data manipulation and programming using tools likeJupyter Notebooks andPandas.
  • Experience with ML frameworkssuch as TensorFlow PyTorch and ScikitLearn for model developmentandtraining.
  • Handson experience with cloudplatforms like AWS (SageMaker Lambda) Azure (ML Studio) or GoogleCloud (AI Platform) for deploying and scaling MLmodels.
  • Knowledge of containerizationusing Docker and orchestration with Kubernetes (K8s) for scalableand automated modeldeployments.
  • Familiarity with CI/CD pipelinesusing tools like Jenkins GitLab CI/CD or CircleCI for automatingthe deployment and integration of MLmodels.
  • Expertise in data engineering andpipeline creation using technologies like Apache Spark ApacheAirflow or Kafka for data processing and workflowautomation.
  • Strong understanding of modelmonitoring and logging using tools like Prometheus Grafana or theELK stack (Elasticsearch Logstash Kibana) to ensure modelperformance inproduction.
  • Experience with version controlusing Git/GitHub and model versioning tools like MLflow DVC or TFX(TensorFlowExtended).
  • Ability to collaborate withcrossfunctional teams using Agile methodologies and tools like JiraSlack or Confluence aligning ML initiatives with businessgoals.
  • Strong problemsolving andanalytical skills for model evaluation optimization and addressingdeployment challenges using techniques like A/B testing hyperparameter tuning and statisticalanalysis.


Benefits
  • 5 Days a week workshift.
  • Work fromhome.


Ability to adapt to newtechnologies, tools, and methods in the rapidly evolving MLOpslandscape. Constant drive to innovate and apply the latest MLresearch to improve processes and solve complex problems.Proficiency in Python and SQL for scripting, data manipulation, andprogramming, using tools like Jupyter Notebooks and Pandas.Experience with ML frameworks such as TensorFlow, PyTorch, andScikit-Learn, for model development and training. Hands-onexperience with cloud platforms like AWS (SageMaker, Lambda), Azure(ML Studio), or Google Cloud (AI Platform) for deploying andscaling ML models. Knowledge of containerization using Docker andorchestration with Kubernetes (K8s) for scalable and automatedmodel deployments. Familiarity with CI/CD pipelines using toolslike Jenkins, GitLab CI/CD, or CircleCI for automating thedeployment and integration of ML models. Expertise in dataengineering and pipeline creation using technologies like ApacheSpark, Apache Airflow, or Kafka for data processing and workflowautomation. Strong understanding of model monitoring and loggingusing tools like Prometheus, Grafana, or the ELK stack(Elasticsearch, Logstash, Kibana) to ensure model performance inproduction. Experience with version control using Git/GitHub andmodel versioning tools like MLflow, DVC, or TFX (TensorFlowExtended). Ability to collaborate with cross-functional teams usingAgile methodologies and tools like Jira, Slack, or Confluence,aligning ML initiatives with business goals. Strong problem-solvingand analytical skills for model evaluation, optimization, andaddressing deployment challenges, using techniques like A/Btesting, hyper parameter tuning, and statisticalanalysis.

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