AI / ML Engineer TECHM-JOB-29139

Keylent, Inc.

$120K — $150K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 7+ years as a Client Engineer with expertise in Python, Data Science, Data Engineering & MLOPS.
  • Strong experience with big data frameworks, specifically Pyspark and GCP tools.
  • Proficient in RESTful API design and implementation.
  • Familiar with web frameworks like FastAPI, Tornado, or Flask.
  • Knowledge of data science libraries such as Pandas, Scikit-learn, TensorFlow, and xgboost.
  • Experience with CI/CD tools, especially Jenkins, for Data Engineering workflows.
  • Understanding of Kubernetes, Docker, and orchestration frameworks like Airflow.

Responsibilities

  • Design, build, and maintain production-grade client applications.
  • Implement data engineering workflows optimizing storage and query costs.
  • Develop and monitor MLOps platforms, managing post-deployment models.
  • Build CI/CD pipelines for deployment of Data Engineering and client jobs.
  • Work with big data systems architected on GCP cloud services.
  • Collaborate on design patterns and architecture for data science best practices.
  • Apply container technologies to enhance machine learning systems.

Benefits

  • Health insurance and wellness programs.
  • Flexible work environment with remote options.
  • Professional development opportunities and training.
  • Paid time off and holiday leave.
  • Retirement savings plan with company match.
Full Job Description
AI / Client Engineer TECHM-JOB-29139
Position :- Client Engineer
Location :- Dallas TX or NJ (Onsite)
Key Skills :- Client/AI, Pysparkand Mlops
Good to have :- GCP

At least 7 years of experience as a Client Engineer, with fluency and experience in Python, Data Science, Data Engineering & MLOPS
Knowledge in RESTful API design and implementation
Experience of Web framework like FastAPI/Tornado/Flask etc.
Data Science knowledge and familiarity with Client libraries such as Pandas, Scikit, TensorFlow, xgboost, time series frameworks like prophet/or equivalent frameworks
In-depth of big data frameworks like Pyspark & gcp tools like data proc, data proc serverless
Experience designing, building and operating productions grade Client applications
Knowledge of design patterns and architecture, data science, and machine learning best practices
Experience with relational databases like Big Query, cloud environments, and a good understanding of optimizing storage cost/query cost while designing data engineering workflows
Good knowledge of Kubernetes, container technologies, docker registries, and applying them in the context of machine learning systems
Working knowledge of Client frameworks, such as Vertex, Kubeflow, MLflow, CloudRun etc.
Experience in designing post-deployment model management framework e.g. model monitoring tools, workflows for feature drift, error analysis of models
Proficiency with CI/CD tools, especially Jenkins
Experience in designing CI/CD pipelines (Jenkins) for deployment of Data Engineering and Client jobs workflow
Hands-on experience in orchestration frameworks like Airflow, Cloud Composer, DataProc Serverless for Pyspark jobs etc.
In-depth understanding of Google Cloud ecosystem for Data Engineering & MLOps - cloud composer, dataproc, dataproc serverless, big query, cloudrun, vertex, vertex pipelines, GKE
Experience in designing MLOps platforms and architect big data systems on GCP cloud

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