Vytwo Technologies Inc

Project Manager

• Develop and manage project assignments through completion
• Communicate results and make recommendations to management
• Provide training
• Identify required resources and research project data
• Generate and analyze statistical data reports
• Communicate activity progress to involved parties
• Resolve issues involving clients when appropriate

IT Project Management Advisor

Software Engineer

• Design, develop, and implement scalable CRM solutions
• Deliver high-quality solutions in collaboration with technical and business teams
• Hands-on development and coordination across multiple workstreams to ensure successful project outcomes.

Dynamics 365 Developer

Backend Engineer

• Multithreading and Synchronization experience is a plus
• Develop and lead .NET applications in a hybrid team environment
• Collaborate with cross-functional teams to implement new features
• Mentor junior developers and provide technical guidance
• Ensure high-availability and performance of applications
• Participate in project planning and architectural design discussions
• Identify bottlenecks and optimize application performance
• Write and maintain unit and integration tests to ensure code quality
• Maintain documentation of application features and architecture

.NET Development Lead – Finance/Banking Experience

Data Engineer

• Design, maintain, and evolve comprehensive data models (Conceptual, Logical, and Physical) for core business services and platforms.
• Define and create data flow diagrams and orchestration strategies, ensuring efficient and governed data movement between up- and downstream systems.
• Collaborate closely with Application and Cloud Architects to ensure the organization’s technical systems are designed to fully support the established data architecture standards.
• Evolve and define data governance policies, standards, and best practices across the organization.
• Design, support, and manage data dictionaries, metadata repositories, and common entities to ensure consistent use and definition of data.
• Drive the standards for Data Quality across multiple datasets, ensuring consistency and data trust across the enterprise.
• Provide technical oversight, advice, and guidance to other Data Architects, Data Engineers, and development teams regarding data structures and associated components.
• Ensure project teams adhere to the corporate data standards and architectural principles set by data leadership.
• Mentor and support colleagues in multi-disciplinary teams, promoting best practices in data architecture and design.

Senior Data Architect

SDET

• Configuring Test Environment order to set it up.
• Participating in Test Environment Setup with an Integrated Development Environment (IDE).
• Automating the design of a framework and Implementing it as per the structure of the project.
• Identifying and selecting the automation test cases.
• Applying various designs and documenting the automation test strategy.
• Creating an automation test plan and getting approval.
• Creating, enhancing, debugging, and running the test cases.
• Collating and monitoring the defect management process.
• Managing the changes and executing regression tests.
• Coming up with exact solutions for problems related to object identity and error handling.
• Interacting with customers/clients to solve the various issues they face and updating on the situation.
• Experience working in onshore / offshore delivery model.
• End – end accountability for teams’ deliverables through the entire SDLC/STLC cycle.

.Net SDET Lead

• Design, develop, and maintain scalable data pipelines using Apache Spark (PySpark and/or Scala)
• Build and optimize data workflows on Databricks , including Delta Lake, notebooks, and scheduled jobs
• Ingest, transform, and curate large-scale structured and semi-structured datasets
• Perform performance tuning and cost optimization of Spark workloads and Databricks clusters
• Implement data quality checks, monitoring, and error handling
• Collaborate with analytics and business stakeholders to deliver well-modeled, analytics-ready data
• Support batch processing and, where applicable, streaming data pipelines
• Follow best practices for testing, documentation, security, and version control

Senior Data Engineer

Data Scientist

• Build, deploy, and optimize ML models for predictive analytics, forecasting, classification, and regression
• Perform large-scale feature engineering using PySpark and Big Data tools
• Work on batch pipelines, model versioning, and experiment tracking
• Develop cost estimation and risk/likelihood models using statistical and ML techniques
• Build NLP pipelines using deep learning frameworks such as PyTorch, TensorFlow, or similar
• Develop real‑time, low‑latency inference systems for text classification, embeddings, semantic search, summarization, and retrieval
• Create prompts, context graphs, and agentic workflows for LLM-based systems
• Apply knowledge of prompt engineering, context engineering, and autonomous agent frameworks to production systems
• Work in Databricks for ETL, feature engineering, ML training, and orchestration
• Use Azure services for model deployment, data pipelines, and infrastructure
• Collaborate using Git-based workflows; leverage tools like GitHub Copilot, Claude Code, etc.
• Implement model monitoring, observability, drift detection, and performance tracking

AI Engineer

• Design and build end-to-end data and AI pipelines using Azure Databricks.
• Develop robust ETL/ELT workflows using Python (PySpark) and SQL.
• Implement CI/CD pipelines for Databricks deployments (jobs, notebooks, workflows).
• Integrate Databricks with Azure services (Data Lake, Blob Storage, Key Vault, Azure OpenAI, Azure Functions, etc.).
• Optimize jobs for performance, cost, and reliability.
• Build reusable, modular code.
• Collaborate with data scientists and platform teams to move models from experimentation to production.
• Implement logging, monitoring, and error handling for production pipelines.
• Develop and deploy ML and Generative AI models (LLMs, embeddings, RAG pipelines) for NLP, computer vision, and predictive analytics.
• Fine-tune LLMs using LoRA/QLoRA and integrate with Azure OpenAI or Hugging Face models.
• Implement vector search and retrieval pipelines using FAISS or Azure Cognitive Search.
• Ensure responsible AI practices, including bias detection and model governance.