Data Architect - R01570398

Brillio 2
Bangalore +1 more
On-site

Who this role is best for

A natural match if you have experience in AI architecture and statistical modeling. This mid-level role in Bangalore emphasizes technical depth in data pipelines, probabilistic models, and cloud deployment.

Best fit for

  • Candidates with 7 to 10 years of experience in data architecture and AI model deployment
    — “With at least 7 to 10 years of experience in data architecture, data science, advanced statistical modeling, and AI architecture”
  • Candidates with hands-on experience in SAS, SPSS, and PySpark for large-scale data processing
    — “Manage and optimize large-scale data processing and AI environments using Python, PySpark, R, and SAS/SPSS”

Things to consider

  • The role requires a strong foundation in statistical analysis and hypothesis testing
    — “Strong knowledge of hypothesis testing, T-Test, and Z-Test”
  • Candidates must be prepared for technical leadership in AI and data architecture adoption
    — “Provide technical leadership in the adoption of emerging technologies and best practices in data architecture, advanced analytics, and AI solutions”

How to stand out

  • Highlight experience in deploying deep learning models and optimizing data workflows
    — “Design and architect robust data pipelines and frameworks to support advanced analytics, machine learning, and AI workloads”
  • Showcase specific projects involving statistical modeling and forecasting techniques like ARIMA and exponential smoothing
    — “Forecasting expertise using ARIMA, ARIMAX, and exponential smoothing”
  • Emphasize leadership in integrating AI models into production systems
    — “Lead the integration of probabilistic graph models, advanced statistical tests (hypothesis testing, T-Test, Z-Test), and AI-driven solutions into production systems”
  • Demonstrate proficiency with data validation tools like Great Expectations and Evidently AI
    — “Ensure data quality and integrity by implementing validation frameworks like Great Expectations and Evidently AI”
  • Demonstrate experience with cloud-based AI model deployment and lifecycle management
    — “Experience with scalable AI model deployment in cloud environments”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Design and architect robust data pipelines and frameworks for advanced analytics, machine learning, and AI workloads
  • Develop and implement statistical and AI models
  • Ensure data quality and integrity by implementing validation frameworks
Typical background
7 to 10 years of experience in data architecture, data science, advanced statistical modeling, and AI architectureAdvanced proficiency in Python and PySparkExpertise in statistical analysis and computing

Skills & requirements

Required

Data-architectureAi-architectureAdvanced-statistical-modelingMachine-learningAi-modelsData-qualityData-validationLarge-scale-data-processingAi-frameworksData-pipelinesAi-deployment

Preferred

KubeflowBentomlTensorFlowPyTorchSci-kit LearnCNTKKerasMxnetRR StudioAutomated-model-monitoringDrift-detectionAi-lifecycle-management

Stack & domain

PythonPysparkSASSPSSGreat ExpectationsEvidently AIKubeflowBentomlTensorFlowPyTorchSci-kit LearnCNTKKerasMxnetArimaExponential SmoothingDecision TreesSVMHammingEuclideanManhattanRR StudioLeadershipProblem-solvingTeamworkCommunicationCuriosityDecision-makingCollaborationCertified Data ProfessionalMicrosoft Certified: Azure Data Scientist AssociateAI Architect CertificationTensorflow Developer CertificateAWS Certified Machine Learning – SpecialtyData ArchitectureAdvanced AnalyticsAIMachine LearningData Science

About the role

Original posting from Brillio 2 via Lever

Data Architect

Job requirements:

Experience Range: With at least 7 to 10 years of experience in data architecture, data science, advanced statistical modeling, and AI architecture Key Responsibilities:

  • Design and architect robust data pipelines and frameworks to support advanced analytics, machine learning, and AI workloads
  • Develop and implement statistical and AI models, including regression (linear and logistic), classification algorithms, forecasting techniques (ARIMA, exponential smoothing), and deep learning architectures
  • Lead the integration of probabilistic graph models, advanced statistical tests (hypothesis testing, T-Test, Z-Test), and AI-driven solutions into production systems
  • Collaborate with data scientists and engineering teams to optimize data workflows and AI model deployment using tools such as KubeFlow and BentoML
  • Ensure data quality and integrity by implementing validation frameworks like Great Expectations and Evidently AI
  • Manage and optimize large-scale data processing and AI environments using Python, PySpark, R, and SAS/SPSS
  • Evaluate and select appropriate machine learning and AI frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet) for scalable model deployment
  • Provide technical leadership in the adoption of emerging technologies and best practices in data architecture, advanced analytics, and AI solutionsRequired Skills:
  • Advanced proficiency in Python and PySpark
  • Expertise in statistical analysis and computing
  • Hands-on experience with SAS and SPSS
  • Strong knowledge of hypothesis testing, T-Test, and Z-Test
  • Experience with regression techniques (linear and logistic)
  • Proficiency in probabilistic graph models
  • Familiarity with Great Expectations and Evidently AI for data validation
  • Forecasting expertise using ARIMA, ARIMAX, and exponential smoothing
  • Working knowledge of KubeFlow and BentoML
  • Experience with classification algorithms (Decision Trees, SVM)
  • Experience architecting AI solutions and deploying deep learning modelsPreferred Skills:
  • Advanced experience with ML and AI frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
  • Expertise in distance metrics (Hamming, Euclidean, Manhattan)
  • Proficiency in R and R Studio
  • Experience with scalable AI model deployment in cloud environments
  • Knowledge of automated model monitoring, drift detection, and AI lifecycle managementDesired Qualifications:
  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
  • Certification in Data Architecture, Data Science, or AI (e.g., Certified Data Professional, Microsoft Certified: Azure Data Scientist Associate, AI Architect certification)
  • Certification in Machine Learning frameworks or platforms (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty)

Source: Brillio 2 careers (Lever)

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