React FullStack & Azure FullStack Developer & MS Power Platform Engineer & AI Engineer (4 Positions)
Job Title: Full Stack Developer ( React)
Location: New York (Hybrid)
Job Summary
We are looking for a skilled Full Stack Developer with strong hands on experience in backend development and React for frontend development. The ideal candidate will work on designing, developing, and maintaining scalable web applications in an Agile environment.
Required Skills
Backend
- Hands on with Spring Boot, Spring MVC, Hibernate/JPA
- Experience developing RESTful web services
- Knowledge of microservices architecture
Frontend
- Strong experience with React
- Proficient in HTML5, CSS3, JavaScript, TypeScript
- Experience with responsive and cross browser UI development
Database & Tools
- Experience with RDBMS (MySQL, PostgreSQL, Oracle)
- Familiarity with Git, Maven/Gradle
- Exposure to CI/CD pipelines is a plus
Job Title: Azure Full Stack Developer
Location: Minneapolis, MN Hybrid Locals only
Interview Mode: (1st will be Teams interview and 2nd will be F2F)
Required Skills:
Azure Full Stack Development, Microsoft Azure, Azure Cloud Services, React.js, Java, Spring Boot, Microservices, REST APIs, Frontend Development, Backend Development, Full Stack Application Development, JavaScript, TypeScript, Application Integration, Cloud Application Deployment, Application Configuration, Containerized Applications, Docker, Container Vulnerability Remediation, Application Security, Snowflake, Snowflake Cortex, Batch Jobs, Application Support, Production Support, Defect Troubleshooting, Root Cause Analysis, Deployment Support, Application Enhancements, Data Updates, Menu and Report Configuration, AI/GenAI Tools, GitHub Copilot, Microsoft 365 Copilot, AI-Assisted Coding, AI-Assisted Debugging, Generative AI, AI-Powered Documentation, Data Analysis, Enterprise AI Tools, Azure DevOps, CI/CD, Version Control, Git, Software Development Lifecycle, Technical Documentation, Customer Support, Stakeholder Communication, Cross-Functional Collaboration, Problem Solving.
Location: New York / Hybrid
Develop and deploy machine learning models using Machine Learning, Cognitive Services, Bot Framework, and other Azure AI tools.
Optimize AI algorithms for performance and scalability.
Conduct thorough testing and validation of AI models to ensure accuracy and reliability.
Document and communicate AI solutions and recommendations to technical and non-technical stakeholders clearly and effectively.
Evaluates AI technologies (such as LLMs, SLMs, embeddings) and architectures (such as orchestration patterns, RAG, etc) when developing solutions. Specifies or implements AI platform improvements like fine-tuning or training custom ML models.
Experience with Power Apps, Power automate, Plugins, AI Builder, CoPilot and related architecture and design practices
Experience with Azure, Azure DevOps for CI/CD and C#.net.
Proven experience in developing and deploying AI models and algorithms.
Proficiency in programming languages such as Python, Java, or C#.
Strong knowledge of AI frameworks and libraries such as TensorFlow, PyTorch, or similar.
Experience in the following technologies Microsoft Dynamic 365 Customer Engagement
Experience of iterative / agile development practices
Job Title: AI Engineer (GenAI / Agentic AI / LLM Engineer)
Location: NY/NJ, Dallas, TX
Required Skills: GenAI, Agentic AI, LLMs, RAG, Production ML Systems, and Python
Job description:
- Bachelor's degree in computer science, Information Technology, or a related field
- Developed Agentic AI applications using Python, LLMs, and RAG for enterprise automation.
- Built scalable Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
- Designed AI agents with tool calling, function execution, and secure workflow orchestration.
- Integrated OpenAI, Claude, Gemini, and Llama models into production applications.
- Implemented prompt engineering, response validation, self-correction, and evaluation frameworks for LLM applications.
- Built production-grade ML pipelines including model deployment, monitoring, inference, and continuous evaluation.
- Integrated AI services with observability, incident management, deployment automation, and production monitoring platforms.
- Deployed scalable AI applications on AWS using Lambda, EKS, SageMaker, S3, DynamoDB, and Terraform.
- Implemented AI governance, guardrails, policy enforcement, rollback strategies, and production safety controls.
- Optimized inference cost and latency using prompt optimization, caching, batching, streaming, and model routing techniques.
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