Cognizant-posted about 22 hours ago
Full-time • Mid Level
West Palm Beach, FL
5,001-10,000 employees

Roles and Responsibilities Cloud & IoT Solutions Implementation (50%25) • Design and implement secure & scalable cloud-native architectures (Microservices, Serverless, Container) using comprehensive AWS stack • Build end-to-end IoT solutions from device connectivity through data analytics • Develop real-time streaming data pipelines from hardware devices and sensors • Design and implement time-series database solutions and data engineering frameworks • Create robust proof-of-concepts (POCs) from ground-up using AWS services Big Data & Analytics Platform Development (25%25) • Implement and optimize big data solutions using Amazon Kinesis, Glue, EMR, Redshift, and Timestream • Design data lakes and warehouses for massive IoT data ingestion and analysis • Build real-time analytics dashboards and reporting frameworks • Optimize query performance and data processing workflows to match business needs • Develop data governance and security frameworks aligning with enterprise guidelines AI/ML Implementation & Innovation (15%25) • Lead implementation of AI-driven solutions for smart grid applications • Develop predictive analytics and anomaly detection systems for grid operations working with Data Scientists • Deploy, optimize and integrate Generative AI, machine learning, deep learning, and natural language processing models for high value use cases • Prototype AI solutions using AWS AI/ML services (SageMaker, Bedrock, etc.) Technical Leadership & Mentoring (10%25) • Guide and mentor junior developers and team members • Lead technical design reviews and architecture decisions • Drive best practices for cloud development and DevOps methodologies • Collaborate with cross-functional teams to translate business requirements into technical solutions • Champion bias-for-action culture while maintaining high technical standards

  • Design and implement secure & scalable cloud-native architectures (Microservices, Serverless, Container) using comprehensive AWS stack
  • Build end-to-end IoT solutions from device connectivity through data analytics
  • Develop real-time streaming data pipelines from hardware devices and sensors
  • Design and implement time-series database solutions and data engineering frameworks
  • Create robust proof-of-concepts (POCs) from ground-up using AWS services
  • Implement and optimize big data solutions using Amazon Kinesis, Glue, EMR, Redshift, and Timestream
  • Design data lakes and warehouses for massive IoT data ingestion and analysis
  • Build real-time analytics dashboards and reporting frameworks
  • Optimize query performance and data processing workflows to match business needs
  • Develop data governance and security frameworks aligning with enterprise guidelines
  • Lead implementation of AI-driven solutions for smart grid applications
  • Develop predictive analytics and anomaly detection systems for grid operations working with Data Scientists
  • Deploy, optimize and integrate Generative AI, machine learning, deep learning, and natural language processing models for high value use cases
  • Prototype AI solutions using AWS AI/ML services (SageMaker, Bedrock, etc.)
  • Guide and mentor junior developers and team members
  • Lead technical design reviews and architecture decisions
  • Drive best practices for cloud development and DevOps methodologies
  • Collaborate with cross-functional teams to translate business requirements into technical solutions
  • Champion bias-for-action culture while maintaining high technical standards
  • AWS cloud services ecosystem (EC2, Lambda, IoT Core, Kinesis, S3, DynamoDB, Redshift, Glue, RDS, Bedrock etc.)
  • Experience with Programming languages (Python, Java, C#, Go, JavaScript/TypeScript)
  • AWS AI/ML services (SageMaker, Bedrock, Comprehend, Rekognition)
  • Big data technologies (Amazon EMR, Redshift, Spark, Hadoop)
  • Deploying machine learning models, deep learning algorithms
  • IoT knowledge including device connectivity, protocols, and data ingestion
  • Real-time streaming data processing and analytics
  • Time-series databases (Amazon Timestream, InfluxDB, or similar)
  • Data engineering and ETL/ELT pipeline development
  • Experience with SonarQube
  • IoT platforms and edge computing solutions
  • Industrial communication protocols (MQTT, OPC-UA, Modbus, DNP3)
  • Containerization and orchestration (Docker, Kubernetes, ECS)
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