Sr. Software Developer
(Plano, TX)

  • Designing, developing, customizing, and implementing enterprise applications utilizing JIRA, Spring, SpringBoot, Microservices, Azure, REST, SQL, IntelliJ, Java, in Spring Framework and cloud environment; conceptual, logical, and physical design of relational database management systems of the applications utilizing SQL/NoSQL DB; configure of Apache Tomcat as web/application servers for deployment of developed application; direct and participate in all aspects of software developed life cycle (SDLC); implement version controls utilizing TFS, VSS, SVN (GitHub).
  • Travel to various unanticipated worksites.
  • Must have bachelor’s in computers / Information Systems / Electronics / Electrical / Related + 5 year exp. as Software Developer / Application Developer / Consultant / System Analyst /Related.
  • Respond to President, almIT Services Inc., 5700 Tennyson Pkwy, Suite # 300, Plano, TX 75024

Case Study 1 : Revolutionizing Manufacturing Operations with Azure IoT Digital Twin

Client Overview:

  • Client Name: TechFab Manufacturing Solutions
  • Industry: Manufacturing
  • Location: Springfield, USA
  • Size: Medium-Scale Manufacturing Facility

Problem Statement: TechFab Manufacturing Solutions, a leading player in the manufacturing industry, faced challenges related to inefficient machinery maintenance and equipment downtime. Their existing maintenance processes were primarily reactive, leading to unexpected production interruptions, increased costs, and reduced overall equipment effectiveness (OEE). They sought a proactive solution to optimize manufacturing operations.


  • ALMIT's team of Azure-certified architects and IoT specialists collaborated with TechFab to design a comprehensive solution.
  • The core of the solution was the implementation of Azure IoT Digital Twin technology, which created virtual representations of physical manufacturing equipment and processes.
  • A network of IoT sensors, including Microsoft Azure IoT Hub, was deployed across critical machinery to continuously collect real-time data on machine health, performance, and environmental conditions.
  • Data from sensors was transmitted securely to Azure IoT Hub for processing and analysis.
  • Azure IoT Central was used to manage and monitor IoT devices, providing a centralized platform for device management and monitoring.

Understanding Azure IoT Digital Twin: A Digital Twin is a virtual representation of a physical object, process, or system. In the context of manufacturing, it means creating a detailed digital replica of manufacturing equipment and processes. This virtual twin is kept in sync with its physical counterpart in real-time, thanks to data from IoT sensors. It allows for:

  • Real-time Monitoring: Engineers can monitor the digital twin to gain insights into the physical equipment's status, health, and performance.
  • Predictive Maintenance: By analyzing historical and real-time data, machine learning models can predict when equipment is likely to fail, enabling proactive maintenance.
  • Simulation and Optimization: Digital Twins can be used to simulate different scenarios, helping optimize processes and improve efficiency.


  • Azure IoT Digital Twins: The ALMIT team configured Azure IoT Digital Twins to mirror the physical equipment, providing a real-time digital replica that allowed for advanced analytics and predictive maintenance.
  • Azure IoT Hub: Data from IoT sensors was securely transmitted to Azure IoT Hub, where it underwent real-time processing, transformation, and analysis.
  • Machine Learning: Advanced machine learning algorithms were employed to predict equipment failures based on historical data and real-time sensor information.
  • Power BI Dashboard: ALMIT created a custom Power BI dashboard for TechFab, providing engineers with a user-friendly interface to monitor equipment health, receive alerts, and plan maintenance proactively.

Solution Diagram:


  • TechFab Manufacturing Solutions experienced significant improvements in manufacturing operations:
    • Equipment downtime was reduced by 30% due to predictive maintenance.
    • Overall Equipment Effectiveness (OEE) increased by 15%.
    • Energy consumption was optimized, resulting in a 20% reduction in energy costs.
    • Production efficiency improved, leading to a 10% increase in output.
    • Maintenance costs decreased by 25%, as resources were allocated more efficiently.

Client Testimonial: "ALMIT's implementation of Azure IoT Digital Twins transformed our manufacturing operations. We now have a proactive approach to maintenance, reducing downtime and costs significantly. The digital twin technology provides invaluable insights into our equipment's health, and the user-friendly Power BI dashboard keeps our engineers informed and in control. We couldn't be happier with the results." - John Smith, COO, TechFab Manufacturing Solutions.

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Case Study 2 : Azure Lakehouse Implementation at FinTech Solutions

Client Overview:

  • Client Name: FinTech Solutions
  • Industry: Financial Services
  • Location: New York City, USA
  • Size: Mid-Sized FinTech Company

Problem Statement: FinTech Solutions was grappling with significant data management challenges due to siloed data storage systems. This resulted in inefficient data processing, delayed analytics, and compliance issues. They needed a unified data platform to enhance decision-making, ensure regulatory compliance, and facilitate advanced analytics and real-time data streaming.

Solution: ALMIT, leveraging its team of Azure-certified architects and data lake specialists, collaborated with FinTech Solutions to develop a comprehensive solution centered around Azure Lakehouse architecture. This approach combined data lake and data warehouse features to provide unified data storage, processing, analytics, reporting, and real-time streaming. Key components included:

  • Azure Data Lake Storage: Centralized repository for structured and unstructured data.
  • Azure Databricks: Employed for data processing and transformation, ensuring data quality and consistency.
  • Azure Synapse Analytics: Unified layer for analytics and reporting, enabling scalable data querying.
  • Azure Stream Analytics: Integrated for real-time data streaming and processing.

Understanding Azure Lakehouse Solution: Azure Lakehouse is a hybrid solution that merges the capabilities of a data lake and a data warehouse. It stores large volumes of raw data (data lake) and offers the structure and speed for advanced analytics, reporting, and real-time streaming (data warehouse).


  • Data Integration: All data sources were integrated into Azure Data Lake Storage.
  • Data Processing: Handled by Azure Databricks for quality and consistency.
  • Analytics and Reporting: Azure Synapse Analytics provided scalable querying and BI.
  • Real-time Streaming: Achieved through Azure Stream Analytics.

Advanced Analytics Component: The solution empowered FinTech Solutions with advanced analytics capabilities, including predictive modeling, machine learning, and real-time streaming analytics.

Data Volume: Designed to manage terabytes of financial data, including transactions, market data, and customer information, the Azure Lakehouse solution ensures scalability with growing data volumes.



  • Data Processing: 40% reduction in processing time.
  • Compliance: Streamlined processes reducing regulatory risks.
  • Data Quality: Enhanced reliability through improved data consistency.
  • Scalability: Architecture capable of handling terabytes of data and real-time streaming.

Client Testimonial: Sarah Johnson, CTO of FinTech Solutions, lauded the implementation, noting significant improvements in data management, analytics, and real-time capabilities. The unified platform provided the flexibility of a data lake with the speed of a data warehouse, improving processing times, compliance, and advanced analytics capabilities.

Conclusion: This case study exemplifies how ALMIT's Azure Lakehouse solutions can revolutionize data management, compliance, advanced analytics, and real-time streaming in the financial services sector, managing substantial data volumes for data-driven decision-making.

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Case Study 3: ALMIT's AI-Powered Audio Prognostics and Diagnostics for MechanoTech Industries

Client Overview:

  • Client Name: MechanoTech Industries
  • Industry: Manufacturing and Equipment Maintenance
  • Location: Stuttgart, Germany
  • Size: Large Manufacturing Company

Problem Statement: MechanoTech Industries, renowned in the field of industrial equipment manufacturing and maintenance, faced critical challenges in predictive maintenance and fault diagnosis. Conventional methods were proving inadequate, leading to extended equipment downtime and increased repair costs. The company sought an advanced solution to enhance its ability to predict equipment failures and accurately diagnose issues in a timely manner.

Solution: ALMIT, known for its expertise in AI and machine learning, collaborated with MechanoTech Industries to develop an innovative audio-based prognostics and diagnostics system. The solution incorporated:

  • Audio Sensing Devices: Installation of sophisticated audio sensors on critical equipment to capture distinct sound signatures.
  • AI-Driven Machine Learning Models: Development of AI algorithms trained to detect and interpret patterns in audio data, correlating them with specific equipment conditions.
  • Real-Time Audio Monitoring: A system for continuous monitoring and real-time analysis of audio data from equipment.
  • Advanced Diagnostic Software: A software platform designed to analyze the audio data and provide diagnostic and prognostic insights.


  • Retrofitting Equipment: Strategic placement of audio sensors on essential equipment for comprehensive sound capture.
  • Continuous Data Harvesting: Implementing a system for the ongoing collection and processing of audio data.
  • AI-Powered Analysis: Utilizing machine learning to identify standard operational sounds and anomalies indicating potential mechanical issues.
  • Intuitive Diagnostic Dashboard: Creating a user-friendly interface for maintenance personnel to receive alerts and detailed diagnostics.

Azure Solution Stack:

  • Azure IoT Edge Devices: Representing the audio sensors installed on the equipment for capturing sound data.
  • Azure IoT Hub: The central point for aggregating audio data from IoT devices.
  • Azure Stream Analytics: Processing real-time audio data streams.
  • Azure AI Services (Cognitive Services/Azure Machine Learning): Analyzing audio data for pattern recognition and anomaly detection.
  • Azure Data Lake Storage: Storing processed and raw audio data.
  • Azure Synapse Analytics: Integrating data storage with advanced analytics.
  • User Interface/Application: A dashboard for maintenance technicians to receive alerts and diagnostics.


  • Decreased Downtime: Proactive detection of potential issues led to a substantial reduction in equipment downtime.
  • Cost Efficiency: Predictive maintenance guided by precise diagnostics resulted in lowered repair expenses.
  • Operational Efficiency: Maintenance teams were able to swiftly identify and resolve equipment issues, enhancing overall efficiency.
  • Innovation in Maintenance: MechanoTech Industries established itself as a pioneer in the field of advanced equipment maintenance through this implementation.

Client Testimonial: "With ALMIT's AI-powered audio diagnostic technology, we've fundamentally transformed our maintenance operations. Our equipment downtime has significantly decreased, and our maintenance costs have been reduced dramatically. The accuracy and speed with which our technicians can now diagnose and resolve issues are unparalleled." - Dr. Hans Becker, CEO, MechanoTech Industries.

This case study exemplifies how ALMIT's innovative AI-driven audio prognostics and diagnostics solution enabled MechanoTech Industries to revolutionize its approach to equipment maintenance, setting a new benchmark in predictive maintenance and diagnostics within the industry.

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Case Study 4 : Revolutionizing Retail Analytics with Azure Lakehouse Integration & LLM

Client Overview:

Client Name: MarketEdge Retailers

Location: Chicago, USA

Size: Large Retail Company

Problem Statement:

MarketEdge Retailers, a prominent entity in the retail sector, encountered operational inefficiencies and data integration challenges due to their reliance on an outdated on-premise SAP system and fragmented data from various third-party sources like Google Analytics. The key issues included inefficient data handling, limited analytics capabilities, and delays in accessing real-time customer insights. To address these challenges, MarketEdge Retailers required an innovative data management system that could integrate multiple data sources into a single, efficient platform, essential for enhancing decision-making processes, customer experience, and overall operational efficiency.


ALMIT's team, composed of Azure-certified specialists and data migration experts, crafted a custom solution for MarketEdge Retailers. This solution entailed a migration from their existing on-premise SAP system to a more sophisticated Azure Lakehouse architecture, effectively unifying their data management and analytics systems.

A pivotal element of this solution was the integration of Azure OpenAI and Databricks, empowering end-users to interact with data through natural language processing (NLP), thus bypassing traditional dashboard-based analytics.

Unique Implementation Details:

  • Azure Lakehouse Framework: A central Azure Data Lake Storage was established, integrating data from both SAP and third-party applications like Google Analytics. This setup offered the benefits of both data lake and data warehouse, ideal for storing large volumes of raw data and facilitating structured data queries for advanced analytics.
  • Azure Databricks: Managed efficient data processing and transformation, maintaining high-quality and consistent data for analytics. It also enabled machine learning and AI functionalities.
  • Azure OpenAI and Databricks Integration: This integration allowed MarketEdge Retailers to use large language models (LLMs) for intuitive data interaction. Users could input natural language queries, which were then translated into data processing tasks by Databricks, democratizing data analytics for non-technical users.
  • Azure Synapse Analytics with Self-Hosted Native SAP ECC Connector: Azure Synapse Analytics was deployed with a self-hosted native SAP ECC connector. This allowed for seamless integration and extraction of data from the SAP ECC system, significantly improving the efficiency and reliability of data transfers.
  • Estimated Savings with SAP BW Module Replacement: By leveraging Azure Synapse Analytics and its SAP ECC connector, MarketEdge Retailers could potentially save millions in software and hardware costs associated with their traditional SAP BW module. This was a major financial benefit, as it streamlined their data warehouse operations and reduced reliance on expensive, proprietary SAP hardware and software.

Results with Metrics:

  • Data to Insights Time Reduction: The transition from data to insights was cut down from weeks or months to minutes, drastically speeding up decision-making processes.
  • Significant Cost Savings: Estimated savings in the millions due to the replacement of the SAP BW module with Azure Synapse Analytics, reducing both software and hardware expenses.
  • Enhanced Real-Time Data Analysis: Improvement in real-time data analysis capabilities by 40%, enabling quicker responses to market trends and customer behavior.
  • Increased Data Interaction by Non-Technical Staff: A 70% rise in data interaction by non-technical staff, indicating improved accessibility and user-friendliness.
  • Reduced Dashboard Development Time: A 60% reduction in the time required for developing and maintaining dashboards.

Client Testimonial:

“The collaboration with ALMIT and the subsequent implementation of the Azure Lakehouse solution, complete with Azure OpenAI and Databricks, has been transformational. The integration of Azure Synapse Analytics with a self-hosted native SAP ECC connector has not only streamlined our data processes but also led to significant financial savings by replacing our traditional SAP BW module. This technological advancement has rapidly accelerated our data-to-insight journey, transforming our decision-making process and enhancing our competitive edge in the retail market.” - Alex Thompson, CIO, MarketEdge Retailers.

This case study highlights ALMIT's skill in deploying Azure Lakehouse solutions, augmented with Azure OpenAI, Databricks, and a specialized Azure Synapse Analytics SAP connector, to significantly enhance data management and analytics for MarketEdge Retailers. This strategy has streamlined operations, provided substantial cost savings, and improved data accessibility and decision-making speed in the retail sector.

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