Sr. Application Developer

Principal Platform Engineering – Digital Integration

Manager, Data Engineering

Sr. Software Engineer - Ag & Trading .Net

Senior Data Engineer

Principal AWS Network Infrastructure Engineer

Senior Data Scientist - Machine Learning

AVP AI Solutions Management

AVP – Model Ops Engineer

Senior Software Engineer - Boomi or BTP

Responsibilities: <ul><li>Design, develop, and maintain web applications using Java, Spring Boot, React, Redux, and PostgreSQL</li><li>Implement and manage APIs and microservices architecture</li><li>Ensure the performance, quality, and responsiveness of applications</li><li>Identify and correct bottlenecks and fix bugs</li><li>Partner with business stakeholders and operations team to identify and manage KPIs</li><li>Evaluate the performance of processes, services, and outcomes and drive performance improvement</li><li>Independently solve complex issues, lead a team of junior and mid-level developers</li><li>Other duties as assigned</li></ul> Requirements: <ul><li>Bachelor’s degree in computer science, Information Technology, or related field. Minimum of 12-15 years offull stack development related work experience</li><li>Strong oral and written communication skills</li><li>Expert in Java, Spring Boot, React, Redux, and PostgreSQL</li><li>Strong analytical skills with the ability to collect, organize, and analyze data</li><li>Attention to detail and accuracy is critical</li><li>Working knowledge of cloud platforms (e.g., AWS, Azure)</li><li>Adept at presenting findings in MS PowerPoint and MS Word</li></ul>

Responsibilities: <ul><li>Technical Leadership &amp; Strategy: Create long-term Integration Strategy and Roadmap, Define and implement Integration patterns &amp; standards</li><li>Technology Evaluation: Research, evaluate, and adopt emerging technologies, tools, and industry trends to enhance integration capabilities</li><li>Solution Architecture: Design scalable and reusable middleware components, including API gateways, message brokers, iPaas and application servers</li><li>Infrastructure Management: Deploy, manage and monitor integration solutions to ensure high availability, optimal performance and minimal downtime</li><li>Governance &amp; Standards: Establish and enforce best practices, security measures, and documentation standards for middleware platforms</li><li>Collaboration &amp; Leadership: Ability to work across teams, mentor engineers, and communicate technical strategies to stakeholders.</li><li>Experience: Substantial experience in multiple middleware technologies for architecture design, software development and system administration in both cloud-native and hybrid environments</li><li>Systems Thinking: Ability to evaluate, design and maintain future ready, secure, scalable digital integration solutions.</li></ul> Requirements: <ul><li>Minimum requirement of 8 years of relevant work experience. Typically reflects 12 years or more of relevant experience.</li><li>Technical Expertise: Strong proficiency with middleware technologies such as</li><li>Integration Platforms: Boomi, SAP BTP, MuleSoft</li><li>API Gateways: Kong, Layer7, Apigee</li><li>B2B/EDI: Open Text, Boomi, IBM Sterling</li><li>Messaging Queue: Kafka, Solace, Pulsar</li><li>Web/App Servers: WebLogic, WebSphere, Tomcat</li><li>Technical Services: Jira, GitHub, Azure DevOps, Datadog, AppDynamics</li></ul>

Responsibilities: <ul><li>DATA &amp; ANALYTICAL SOLUTIONS: Oversees the development of data products and solutions using big data and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.</li><li>DATA PIPELINES: Develops and monitors streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.</li><li>DATA SYSTEMS: Reviews existing data systems and architectures to lead identification of areas for improvement and optimization.</li><li>DATA INFRASTRUCTURE: Oversees the preparation of data infrastructure to drive the efficient storage and retrieval of data.</li><li>DATA FORMATS: Reviews and resolves appropriate data formats to improve data usability and accessibility across the organization.</li><li>STAKEHOLDER MANAGEMENT: Partners collaboratively with multi-functional data and advanced analytic teams to capture requirements and ensure that data solutions meet the functional and non-functional needs of various partners.</li><li>DATA FRAMEWORKS: Builds complex prototypes to test new concepts and provides guidance to implement data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.</li><li>AUTOMATED DEPLOYMENT PIPELINES: Oversees the development of automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.</li><li>DATA MODELING: Guides the team to perform data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.</li><li>TEAM MANAGEMENT: Manages team members to achieve the organizations goals, by ensuring productivity, communicating performance expectations, creating goal alignment, giving and seeking feedback, providing coaching, measuring progress and holding people accountable, supporting employee development, recognizing achievement and lessons learned, and developing enabling conditions for talent to thrive in an inclusive team culture.</li></ul> Requirements: <ul><li>Some experience in React, including hooks, context API, and state management libraries (Redux, Zustand)</li><li>Solid experience with JavaScript (ES6+), TypeScript, HTML5, CSS3.</li><li>Strong experience with C# and .NET Framework/.NET Core.</li><li>Solid understanding of object-oriented programming.</li><li>Experience with unit testing and mocking frameworks (e.g., xUnit, NUnit, Moq).</li><li>Familiarity with design patterns (e.g., Repository, Factory, Strategy).</li><li>Experience with SQL DBs, MySQL, Postgres, or MS SQL: writing queries, stored procedures, and performancetuning.</li><li>Knowledge of RESTful APIs and message-based systems.</li><li>Experience with relational databases and data access libraries (e.g., Entity Framework, Dapper).</li><li>Knowledge of ReactJS</li><li>Proficient in Git: branching, merging, pull requests, and version control workflows.</li></ul>

Responsibilities: <ul><li>Demonstrate accountability by ensuring timely and high-quality project delivery.</li><li>Be result driven, consistently achieving measurable outcomes and meeting goals.</li><li>Act as a team player, collaborating effectively with cross-functional teams.</li><li>Exhibit a can-do mentality, tackling challenges with a proactive and positive approach.</li><li>Apply software engineering patterns and principles to design, develop, test, integrate, maintain, troubleshoot and document complex and varied software solutions and incorporate security practices in newly developed and maintained applications.</li><li>Participate in peer code review sessions to review code, coach peers and ensure code quality.</li><li>Take the lead in the assigned agile team to adopt agile philosophies, facilitate agile ceremonies and identify continuous improvement opportunities.</li><li>Incorporate the companys engineering and development best practices within the full software development lifecycle including coding standards, code reviews, source control management, building processes, testing and security principles, to deliver high quality code rapidly.</li><li>Collaborate to lead demonstration and continuous feedback sessions to improve development and help drive the long term vision.</li><li>Participate in the engineering community by maintaining and sharing relevant technical approaches and modern skills.</li><li>Independently handle complex issues with minimal supervision, while escalating only the most complex issues to appropriate staff.</li><li>Other duties as assigned</li></ul> Requirements: <ul><li>Bachelor’s degree in a related field or equivalent experience</li><li>Confirmed experience automating pipelines for continuous integration, testing, delivery and security.</li><li>Confirmed experience architecting applications, databases, services or integrations.</li><li>Extensive experience with .NET Framework and .NET Core.</li><li>Familiarity with front-end technologies like HTML, CSS, JavaScript, and frameworks such as Angular, React, or Vue.js.</li><li>Experience with DevOps practices and tools such as Docker and CI/CD pipelines.</li><li>Knowledge of SQL Server and / or PostgreSQL for database design and management.</li><li>Hands-on experience with Kubernetes for container orchestration and management.</li><li>Experience with cloud platforms such as Azure, AWS, or GCP.</li><li>Minimum of 5 years of related work experience in software development using .NET technologies.</li></ul>

Responsibilities: Data Infrastructure:&nbsp; <ul><li>Prepares data infrastructure to support the efficient storage and retrieval of data.</li></ul> Data Formats:&nbsp; <ul><li>Examines and resolves appropriate data formats to improve data usability and accessibility across the organization.</li></ul> Data &amp; Analytical Solutions:&nbsp;&nbsp; <ul><li>Develops complex data products and solutions using advanced engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.</li></ul> Data Pipelines:&nbsp;&nbsp; <ul><li>Develops and maintains streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.</li></ul> Data Systems:&nbsp;&nbsp; <ul><li>Reviews existing data systems and architectures to identify areas for improvement and optimization.</li></ul> Stakeholder Management: <ul><li>Collaborates with multi-functional data and advanced analytic teams as well as with business teams to gain requirements and ensure that data solutions meet the functional and non-functional needs of various partners.</li></ul> Data Frameworks:&nbsp; <ul><li>Builds complex prototypes to test new concepts and implements data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.</li></ul> Automated Deployment Pipelines:&nbsp;&nbsp; <ul><li>Develops automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.</li></ul> Data Modeling:&nbsp;&nbsp; <ul><li>Performs complex data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.</li></ul> Requirements: <ul><li>Minimum requirement of 4 years of relevant work experience.&nbsp;</li><li>Typically reflects 5 years or more of relevant experience.</li></ul>

Responsibilities: Design and Implementation:&nbsp; <ul><li>Architect and deploy scalable, secure, and highly available network infrastructure solutions in AWS</li></ul> Network Management:&nbsp; <ul><li>Manage and optimize AWS networking services such as VPC, Direct Connect, Route 53, CloudWAN, and Transit Gateway</li></ul> Security:&nbsp; <ul><li>Implement and maintain network security best practices, including firewalls, VPNs, and security groups</li></ul> Automation:&nbsp; <ul><li>Develop and maintain automation scripts using tools like Terraform, CloudFormation, and Python</li></ul> Monitoring and Troubleshooting:&nbsp; <ul><li>Monitor network performance and troubleshoot issues to ensure optimal performance and reliability</li></ul> Collaboration:&nbsp; <ul><li>Work with cross-functional teams to support application development and deployment</li></ul> Documentation:&nbsp; <ul><li>Create and maintain detailed network documentation and diagrams</li></ul> Cost Management:&nbsp; <ul><li>Monitor and optimize cloud costs, implementing cost-saving measures where possible</li></ul> Compliance and Governance:&nbsp; <ul><li>Ensure that all cloud network solutions comply with our client standards</li></ul> Performance Tuning:&nbsp; <ul><li>Continuously improve the performance and reliability of network systems</li></ul> Incident Response:&nbsp; <ul><li>Lead incident response efforts for network-related issues and outages</li></ul> Training and Mentorship:&nbsp; <ul><li>Provide training and mentorship to junior network engineers and other team members</li></ul> Innovation/Continuous Learning:&nbsp; <ul><li>Stay updated with the latest cloud networking technologies and propose innovative solutions to improve the infrastructure</li></ul> Business Competencies: <ul><li class="ql-indent-1">Adaptable to work effectively within a variety of situations and with various individuals, groups or independently</li><li class="ql-indent-1">Ability to document and communicate effectively, explaining technical concepts and network initiatives in business terms in both individual and group settings</li><li class="ql-indent-1">Must be tenacious, overcoming obstacles and identifying risks</li><li class="ql-indent-1">Attention to detail will be critical to assist with the successful implementation of initiatives</li></ul> Requirements: <ul><li class="ql-indent-1">10+ years of experience in IT, with at least 5+ years in AWS cloud engineering and architecture.</li><li class="ql-indent-1">Strong understanding of AWS network infrastructure and service integration, including VPC, IAM, Transit Gateway, and CloudFormation, and how AWS services such as EC2, EKS, RDS, S3, and Lambda operate within those network architectures.</li><li class="ql-indent-1">Strong experience with Terraform, CloudFormation, and automation scripting (Bash, Python, PowerShell).</li><li class="ql-indent-1">Proficiency in monitoring and logging tools (DataDog, Splunk, Coralogix, CloudWatch).</li></ul> Technical Skills: <ul><li class="ql-indent-1">In-depth knowledge of AWS networking services and best practices, such as VPC, AWS Firewall, Transit Gateway, WAF, Security Groups, CloudWAN, NACLS, Shield Advance, and Direct Connect</li><li class="ql-indent-1">Advanced understanding of networking protocols and services such as BGP, OSPF, TCP/IP, DNS, DHCP, and VPNs</li><li class="ql-indent-1">Proficiency in network security principles and tools</li><li class="ql-indent-1">Skilled in network automation and scripting</li><li class="ql-indent-1">Experience in developing IaC with advanced skills in tools such as Terraform, AWS CloudFormation, and Ansible for automating infrastructure deployment and management</li><li class="ql-indent-1">Familiar with DevOps practices / CI/CD pipelines using tools like Jenkins, GitLab CI, or GitHub Actions to automate testing and deployment</li></ul>&nbsp;

Responsibilities: <ul><li>Lead the design, development, and deployment of machine learning models using Python for applications including process optimization, predictive analytics, demand forecasting, computer vision, and decision support systems</li><li>Network across manufacturing, R&amp;D, commercial, and business functions to identify and collaboratively execute on high-impact challenges solvable through machine learning techniques</li><li>Build production-ready ML solutions within enterprise platforms (Databricks, Azure ML) including model training, endpoint deployment, and monitoring</li><li>Develop and maintain scalable, modular, well-documented code following MLOps best practices including version control, logging/metadata standards, and CI/CD principles</li><li>Lead code reviews, architectural discussions, and knowledge sharing to maintain high quality, production-ready deliverables across the team</li><li>Collaborate with domain experts and stakeholders across a wide variety of domains to understand needs, integrate ML solutions into workflows, and deliver measurable business value</li><li>Communicate complex machine learning methodology, results, and business impact clearly to technical and non-technical audiences through presentations, documentation, and visualizations</li><li>Stay current with advancements in machine learning, and apply them to solve business challenges</li><li>Mentor and provide technical guidance to data scientists, helping to grow the teams capabilities in Hyderabad</li><li>Support ML governance processes including architecture and methodology reviews, risk assessments, and compliance with enterprise standards</li><li>Lead project planning and execution, managing timelines, resources, and stakeholder expectations</li></ul> Requirements: <ul><li>Masters or Ph.D. in a relevant field such as Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a domain-focused field (Engineering, Chemistry, Economics, Finance, etc.)</li><li>5-8+ years of experience in data science, machine learning, or a related field with demonstrated impact</li><li>Strong foundation in statistical and machine learning theory with proven ability to select, apply, and evaluate appropriate techniques for complex, real-world problems</li><li>Extensive hands-on experience building predictive models and deploying them to production environments using Python</li><li>Proficiency with modern ML frameworks (scikit-learn, XGBoost/CatBoost, TensorFlow/PyTorch) and data manipulation libraries (pandas, polars, numpy)</li><li>Experience with solution architecting and design for ML systems</li><li>Hands-on experience with cloud platforms such as Azure and/or Databricks for deploying and managing ML models and solutions</li><li>Experience with version control (Git), collaborative development, and code review processes</li><li>&nbsp;Strong written and oral communication skills with ability to present technical concepts and results to diverse audiences from technical teams to business leadership</li><li>History of project leadership and driving initiatives from conception to production</li><li>Strong problem-solving skills and the ability to work independently as well as lead and mentor team members&nbsp;</li></ul>

Responsibilities: <ul><li>Support the AI use-case intake process, gather requirements, and rapidly assess strategic fit, value, complexity, and feasibility.</li><li>Maintain an AI use-case/solution inventory; identify duplication and promote reuse of existing platforms and components.</li><li>Evaluate alignment to the company’s AI strategy, enterprise architecture, data strategy, and target operating model.</li><li>Help establish guidelines for build/buy/hybrid and create recommendation artifacts for leadership and governance bodies.</li><li>Assess candidate architectures across cloud services, data platforms, model choices (LLMs, classical ML, CV, NLP), and integration patterns.</li><li>Compare third-party SaaS/managed services with in-house custom builds and hybrids; estimate TCO, scalability, maintainability, and time-to-value.</li><li>Support vendor evaluations and small proof-of-concept efforts, reviewing items like security practices, data handling, SLAs, integration needs, and roadmap alignment.</li><li>Partner with Procurement/Vendor Management on contracting, licensing, data processing, and exit strategies.</li><li>Partner with Engineering, Data Science, and DevOps/MLOps to scope pilots and define success metrics and guardrails.</li><li>Ensure handoffs include documentation, controls, and operational readiness</li><li>Develop playbooks, patterns, and templates to support AI solution reviews, and help enable business and tech teams through training sessions and office hours..</li><li>Track portfolio outcomes (benefits, risk findings, reuse rate) and continuously improve intake and evaluation processes.</li></ul> Requirements: <ul><li>Software engineering and cloud architecture knowledge across AWS/Azure; APIs; event/microservices; observability.</li><li>Good understanding of AI/ML and LLM stacks (vector databases, embeddings, RAG, prompt engineering, guardrails).</li><li>Understanding of privacy/security controls in financial services (data classification, encryption, DLP, IAM, segmentation) and third-party risk.</li><li>Proven ability to translate business objectives into technical evaluation criteria and clear decisions.</li><li>Excellent written and verbal communication for executive-level summaries and deep-dive technical reviews.</li><li>Critical thinker with structured problem-solving and sound judgment</li></ul>

Responsibilities: <ul><li>Design, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).</li><li>Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices.</li><li>Develop reusable monitoring components (e.g., performance drift detectors, threshold-based alerts, metric repositories) that support various model types and regulatory needs.</li><li>Integrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to ensure seamless model performance tracking.</li><li>Partner with model risk and compliance teams to ensure data lineage, audit trails, and documentation are preserved and accessible for regulatory reviews (e.g., SR 11-7 compliance).</li><li>Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements.</li><li>Work closely with the model monitoring analytics and strategy monitoring analytics teams within MO&amp;A to ensure the monitoring data infrastructure adapts to changing analytics and monitoring needs. Enable visualization and reporting capabilities through dashboards (e.g., Power BI, Tableau) that summarize model health, stability, and issue alerts.</li><li>Designing and maintaining high-performance data pipelines that ingest, transform, and version datasets for Model and Strategy Monitoring</li><li>Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles.</li></ul> Requirements: <ul><li>Bachelor’s degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 5+ years’ experience or in lieu of degree, and 7+ years of relevant work experience in, data engineering or related roles in the financial services or regulated analytics domain.</li><li>Strong proficiency with data engineering tools and frameworks (e.g., Apache Spark, Airflow, Kafka, dbt, PySpark).</li><li>Proficient in programming languages such as SAS, Python, and SQL for building monitoring pipelines and validation checks.</li><li>Experience with cloud-based data infrastructure (e.g., AWS, Azure, GCP) and data warehousing (e.g., Snowflake, Redshift, BigQuery).</li><li>Familiarity with MLOps practices, model metadata tracking (e.g., MLflow), and monitoring toolkits (e.g., Evidently AI, Why Labs, Prometheus).</li><li>Understanding of model risk governance requirements and the role of data engineering in ensuring compliant model monitoring.</li><li>Ability to work in an agile environment and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams.</li></ul>

Responsibilities: <ul><li>SOFTWARE DEVELOPMENT: Designs and develops high quality software solutions by writing clean, maintainable and efficient codes.</li><li>AUTOMATION: Leads the application of internal software deployment platform, methodologies and tools to automate the deployment process, ensuring smooth and reliable releases.</li><li>COLLABORATION: Partners with cross functional team of product managers, designers and different engineers to gather complex requirements and deliver solutions that meet business needs.</li><li>TESTING &amp; DEBUGGING: Writes and maintains complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software.</li><li>CONTINUOUS IMPROVEMENT: Suggests options for improving the software development and deployment processes, and implements the approved standards to improve efficiency and reliability.</li><li>DOCUMENTATION: Builds and maintains comprehensive documentation for complex software applications, deployment processes and system configurations.</li><li>TECHNICAL SUPPORT: Provides technical support and troubleshooting for complex issues with deployed applications to ensure minimal downtime and fast resolution.</li></ul> Requirements: <ul><li>Minimum requirement of 4 years of relevant work experience.</li><li>Typically reflects 5 years or more of relevant experience.</li><li>SAP BTP and/or Boomi expertise</li><li>7+ years of professional experience</li></ul>

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48 dot net cloud developer Jobs found

Sr. Application Developer

Principal Platform Engineering – Digital Integration

Manager, Data Engineering

Sr. Software Engineer - Ag & Trading .Net

Senior Data Engineer

Principal AWS Network Infrastructure Engineer

Senior Data Scientist - Machine Learning

AVP AI Solutions Management

AVP – Model Ops Engineer

Senior Software Engineer - Boomi or BTP

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48 dot net cloud developer Jobs found

Sr. Application Developer

Principal Platform Engineering – Digital Integration

Manager, Data Engineering

Sr. Software Engineer - Ag & Trading .Net

Senior Data Engineer

Principal AWS Network Infrastructure Engineer

Senior Data Scientist - Machine Learning

AVP AI Solutions Management

AVP – Model Ops Engineer

Senior Software Engineer - Boomi or BTP

WhiteCrow WhiteCrow is on a mission to connect companies with the best talent across the globe.

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WhiteCrow is on a mission to connect companies with the best talent across the globe.