Full Stack Cloud Engineer-PV W2

Job Description -

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Full Stack Cloud Engineer-PV W2
Hybrid- Palm Beach, FL
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Need Local candidate

Need Candidate with energy domain

Need Experience with Energy/Utility Client

Please submit candidate with Grid/SmartGrid Experience and answers of the below questions from the candidate side.

Client seeking an experienced Senior Full Stack Cloud Engineer to lead technical innovation within our Smart Grid 2.0 initiative. This senior-level position requires a technical leader who can design and implement highly secure, end-to-end cloud solutions, from IoT device integration to AI-powered analytics platforms. The ideal candidate combines deep AWS expertise with hands-on IoT experience and AI/ML implementation skills, capable of building sophisticated proof-of-concepts while mentoring development teams in a fast-paced, mission-critical environment.
Essential Functions s Responsibilities
Cloud s IoT Solutions Implementation (50%)
Design and implement secure C 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 s Analytics Platform Development (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 s Innovation (15%)
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 s Mentoring (10%)
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
Required Qualifications Educations/ Experience:
Bachelor's degree in Computer Science, Electrical Engineering, or related technical field; Master's preferred
Minimum 8-10 years of full-stack software development experience
At least 5 years of hands-on AWS cloud platform experience with developing solutions using multiple services
Minimum 3 years leading technical teams or mentoring developers
Core Technical Skills:
Expert-level knowledge of AWS cloud services ecosystem (EC2, Lambda, IoT Core, Kinesis, S3, DynamoDB, Redshift, Glue, RDS, Bedrock etc.)
Proven experience with real-time streaming data processing and analytics
Hands-on expertise with time-series databases (Amazon Timestream, InfluxDB, or similar)
Strong background in data engineering and ETL/ELT pipeline development
Proficiency in multiple programming languages (Python, Java, C#, Go, JavaScript/TypeScript)
Solid understanding of DevOps practices with hands-on experience using SonarQube(or similar) to drive code quality and developer productivity
AI/ML s Big Data Expertise:
Demonstrated experience implementing AI-driven solutions in production environments
Hands-on experience with AWS AI/ML services (SageMaker, Bedrock, Comprehend, Rekognition)
Proven track record with big data technologies (Amazon EMR, Redshift, Spark, Hadoop)
Experience deploying machine learning models, deep learning algorithms
IoT s Systems Integration:
Deep hands-on IoT knowledge including device connectivity, protocols, and data ingestion
Experience with IoT platforms and edge computing solutions
Understanding of industrial communication protocols (MQTT, OPC-UA, Modbus, DNP3)
Proficiency in containerization and orchestration (Docker, Kubernetes, ECS)