Company Description At Everite Solutions, we drive innovation through a blend of cutting-edge IT products, comprehensive services, expert staffing solutions, and transformative digital strategies. Whether delivering custom software, providing IT consulting, sourcing top-tier talent, or guiding businesses through digital transformation, we empower organizations to thrive in a rapidly evolving tech landscape. Our mission is to help achieve operational excellence and sustainable growth with tailored, technology-driven solutions. Role Description We are looking for a full-stack engineering powerhouse who can design, develop, and deploy complex, scalable, and high-availability software solutions from scratch. The role requires hands-on expertise across the entire software development lifecycle from system design and API architecture to mobile app deployment and cloud-native scaling. You’ll be expected to own the stack: web, mobile, APIs, databases, event-driven messaging, cloud deployments, monitoring, and AI/ML integration. Core Responsibilities • Architect and implement modular, maintainable, and scalable full-stack applications. • Build responsive, high-performance front-ends using React with TypeScript, advanced state management (Redux, MobX, or Context API), and server-side rendering (Next.js). • Develop cross-platform mobile applications with Flutter (Dart) and native Swift modules for deep iOS integration. • Create robust RESTful APIs and GraphQL endpoints with Java (Spring Boot) — including request validation, rate limiting, and versioning strategies. • Implement API gateways, request routing, and service discovery for microservices architectures. • Manage real-time communication with WebSockets/Socket.IO and streaming APIs. • Design and maintain event-driven systems using Apache Kafka (including topics, partitions, key-based ordering, retention policies) or RabbitMQ. • Optimize API performance through caching (Redis, Memcached), compression, and async processing. • Implement authentication and authorization flows (OAuth2, OpenID Connect, JWT) with multi-factor authentication support. • Administer SQL databases (PostgreSQL/MySQL) — indexing, query optimization, sharding, replication — and NoSQL databases (MongoDB, DynamoDB, Redis). • Build ETL data pipelines and integrate with third-party APIs (REST, GraphQL, gRPC). • Integrate AI/ML features via TensorFlow, PyTorch, or cloud-native AI APIs (AWS SageMaker, Azure Cognitive Services, OpenAI API). • Develop and maintain CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI) with zero-downtime deployment strategies. • Deploy workloads on AWS (EKS, ECS, Lambda, S3, RDS, DynamoDB, CloudFront, API Gateway) and Azure (AKS, Functions, Cosmos DB, Application Gateway). • Orchestrate workloads using Kubernetes (Helm charts, operators, HPA, ingress controllers). • Define and manage Infrastructure as Code with Terraform and/or AWS CloudFormation. • Implement monitoring and observability using Prometheus, Grafana, ELK Stack, and distributed tracing tools like Jaeger or OpenTelemetry. • Apply security best practices for code, APIs, databases, and cloud deployments — including encryption at rest & in transit, secrets management (Vault, AWS Secrets Manager), and vulnerability scanning. • Perform load testing, performance benchmarking, and horizontal/vertical scaling strategies. • Troubleshoot distributed system failures, identify bottlenecks, and implement fixes without service downtime. Required Technical Skills • Front-End: React (TypeScript), Next.js, CSS-in-JS (Styled Components/Emotion), responsive design. • Mobile: Flutter (Dart), Swift, Xcode, native module integration. • Back-End: Java (Spring Boot), REST, GraphQL, gRPC, async programming patterns. • APIs: API design principles (OpenAPI/Swagger), API gateway configuration, API lifecycle management. • Databases: PostgreSQL/MySQL, MongoDB, DynamoDB, Redis — query optimization, schema design, migrations. • Messaging/Event Systems: Kafka (topics, partitions, schema registry, consumers), RabbitMQ, AWS EventBridge. • Cloud & DevOps: AWS, Azure, Kubernetes, Docker, Terraform, CI/CD pipelines, Helm. • Security: OAuth2, OpenID Connect, JWT, HTTPS/TLS, OWASP Top 10. • AI/ML Integration: TensorFlow, PyTorch, cloud AI APIs. • Performance: Profiling, caching, load testing, CDN integration. Nice-to-Have Extras • Experience with microservices choreography and saga patterns. • Knowledge of serverless event processing (AWS Lambda, Azure Functions). • Understanding of multi-region deployments and disaster recovery strategies. • Familiarity with data streaming analytics (Apache Flink, Spark Streaming). • Hands-on with observability at scale — log aggregation, metric alerting, distributed tracing. What We Expect Beyond Code • Critical thinking: Understand not just “how” but “why” in every technical decision. • Independence: Ability to take a loosely defined requirement and ship a production-ready solution. • System-level thinking: Consider scalability, cost, and maintainability in every design. • Strong debugging mindset: Diagnose and solve complex issues under
Company Description At Everite, we drive innovation through a blend of cutting-edge IT products, comprehensive services, expert staffing solutions, and transformative digital strategies. We deliver custom software, provide IT consulting, source top-tier talent, and guide businesses through digital transformation. Our mission is to help organizations achieve operational excellence and sustainable growth with tailored, technology-driven solutions. About the Role: We're looking for a meticulous QA Engineer to lead testing efforts for our multi-agent AI system. This is a cross-functional QA role where you’ll validate everything from AI-generated decisions to backend APIs, UI components, and third-party integrations (voice cloning, Twilio). You’ll create and run test plans for 8+ autonomous agents, including LLM-backed agents, and help ensure every part of the system behaves reliably and ethically — before, during, and after deployment. Key Responsibilities: Manual & Exploratory Testing Understand agent workflows. Perform manual testing of workflows across: Web UI interfaces LLM responses and validation triggers Communications (email, SMS, voice) Backend API changes Identify issues in agent decision logic (false positives, incorrect matching, onboarding gaps, etc.). Automated Testing Design and implement automated test scripts for: API endpoints Event/job creation flows Onboarding and contract handling SMS/Email/Voice triggers and responses Use tools like Postman, Selenium, Playwright, or PyTest for scripting. Test Planning & Documentation Write and manage: Test cases and scenarios for each AI agent. Test Data and Synthetic data preparation (with support from ML team) Regression, functional, and smoke test plans. Edge-case tests for fallbacks, failed calls, no-shows, etc. Document known issues, test coverage, and agent-specific behavior. QA Reporting Track bugs and raise detailed Jira tickets for: UI/UX inconsistencies API response mismatches Communication errors Model prediction issues (with support from ML team) Report test progress and bug severity regularly to the engineering team. Collaboration & Feedback Loops Collaborate closely with: AI Engineers to validate LLM and ML behavior. Backend Devs for API consistency and error handling. Voice & Communication Engineers to validate cloned call quality and integrations. Help define agent success metrics from a QA lens (e.g., no incorrect crew matches, no duplicate invites, etc.) Required Skills & Qualifications: 3–5+ years in QA Engineering or SDET roles Solid understanding of API and integration testing Experience with test automation tools: Postman / Swagger Selenium Python-based testing frameworks (PyTest preferred) Knowledge of version control (Git/Bitbucket) and issue tracking (Jira Issues) Preferred (Bonus) Experience: Testing systems with AI/ML components or LLM-based applications Familiarity with voice API testing (e.g., Twilio Voice, ElevenLabs output validation) Worked on event-based or on-demand staffing platforms Understanding of CI/CD and QA automation pipelines Tools You May Work With: Automation & API Tools: Postman, PyTest, Playwright, Selenium Bug Tracking: GitHub Projects, Jira Monitoring Feedback: Azure Monitor / Log Analytics Voice & Message Validation: Call transcripts, email/SMS message logs Other: Google Sheets (Test tracking), Teams (Internal team comms) Qualifications Experience in Test Execution, Manual Testing, and Software Testing Proficiency in developing and executing Test Cases Strong Quality Assurance skills and ability to document defects effectively Excellent communication and collaboration skills Detail-oriented with strong analytical skills Knowledge of automation tools and scripting is a plus Bachelor's degree in Computer Science, Information Technology, or related field
Machine Learning (ML) Full Stack Engineer: AI Agent System About the Role: We are seeking a talented Machine Learning Engineer to help design, train, and deploy key ML components of our AI Agentic system, which automates crew selection for live events. Youll work on two core areas: Fine-tuning LLMs for natural-language event creation. Building ranking models for human resources selection based on performance, availability, and feedback. Youll collaborate with our Data Analyst, AI Engineers, and Backend team to deliver robust, explainable, and production-ready ML systems. Key Responsibilities: Model Development Fine-tune open-source LLMs (e.g., LLaMA 2, DeepSeek, Mistral) using domain-specific data (event records, job descriptions). Build a Ranker model that scores and ranks crew members using: Historical performance Event attendance Ratings, notes, and feedback Availability data Cancellation rates (negatively weighted) Data Work Collaborate with Data Analysts to clean, preprocess, and label datasets for ML training. Develop robust feature engineering pipelines and transformation logic. Evaluation & Tuning Experiment with different ML algorithms (XGBoost, Random Forest, or Neural Nets) for ranking. Evaluate model performance using precision, recall, accuracy, and business-centric metrics (quality of match). Run A/B testing or simulated matching if applicable. Deployment Support Export trained models in a form consumable by AI Agents (ONNX, pickle, TorchScript, etc.). Work with Backend/DevOps teams to ensure model inference is performant and scalable. Documentation Create technical documentation for model logic, assumptions, and explainability. Provide guidelines for continuous improvement based on feedback loops. Required Skills & Qualifications: 3+ years of experience in machine learning / AI development Strong understanding of: Supervised learning (ranking, classification) NLP techniques (tokenization, embedding models, prompt engineering) Fine-tuning LLMs using LoRA or PEFT frameworks Hands-on with ML libraries: PyTorch, Transformers (Hugging Face), scikit-learn, XGBoost Strong coding skills in Python Experience building, evaluating, and tuning real-world ML models Preferred candidate profile Experience working with event scheduling, HR tech, or workforce automation Familiarity with RAG systems (Retrieval Augmented Generation) Knowledge of MLOps or deployment workflows Use of GPU-enabled training environments, especially in Azure or Colab Pro, OpenAI Tools You May Use: Hugging Face Transformers, Datasets PyTorch / TensorFlow Scikit-learn, XGBoost Azure ML / Google Colab / Jupyter MLflow (optional for tracking) Pandas, NumPy GitHub / Bitbucket for version control Role & responsibilities
We're looking for a meticulous QA Engineer to lead testing efforts for our multi-agent AI system that automates crew member matching, onboarding, and job tracking. This is a cross-functional QA role where youll validate everything from AI-generated decisions to backend APIs, UI components, and third-party integrations (voice cloning, Twilio). Youll create and run test plans for 8+ autonomous agents, including LLM-backed agents, and help ensure every part of the system behaves reliably and ethically before, during, and after deployment. Key Responsibilities: Manual & Exploratory Testing Understand agent workflows (Job Matcher, Onboarding Agent). Perform manual testing of workflows across: Web UI interfaces LLM responses and validation triggers Communications (email, SMS, voice) Backend API changes Identify issues in agent decision logic (false positives, incorrect matching, onboarding gaps, etc.). Automated Testing Design and implement automated test scripts for: API endpoints Event/job creation flows Onboarding and contract handling SMS/Email/Voice triggers and responses Use tools like Postman, Selenium, Playwright, or PyTest for scripting. Test Planning & Documentation Write and manage: Test cases and scenarios for each AI agent. Test Data and Synthetic data preparation (with support from ML team) Regression, functional, and smoke test plans. Edge-case tests for fallbacks, failed calls, no-shows, etc. Document known issues, test coverage, and agent-specific behavior. QA Reporting Track bugs and raise detailed Jira tickets for: UI/UX inconsistencies API response mismatches Communication errors Model prediction issues (with support from ML team) Report test progress and bug severity regularly to the engineering team. Collaboration & Feedback Loops Collaborate closely with: AI Engineers to validate LLM and ML behavior. Backend Devs for API consistency and error handling. Voice & Communication Engineers to validate cloned call quality and integrations. Help define agent success metrics from a QA lens (e.g., no incorrect crew matches, no duplicate invites, etc.). Required Skills & Qualifications: 3-5+ years in QA Engineering or SDET roles Solid understanding of API and integration testing Experience with test automation tools: Postman / Swagger Selenium Python-based testing frameworks (PyTest preferred) Knowledge of version control (Git/Bitbucket) and issue tracking (Jira Issues) Preferred (Bonus) Experience: Testing systems with AI/ML components or LLM-based applications Familiarity with voice API testing (e.g., Twilio Voice, ElevenLabs output validation) Worked on event-based or on-demand staffing platforms Understanding of CI/CD and QA automation pipelines Tools You May Work With: Automation & API Tools: Postman, PyTest, Playwright, Selenium Bug Tracking: GitHub Projects, Jira Monitoring Feedback: Azure Monitor / Log Analytics Voice & Message Validation: Call transcripts, email/SMS message logs Other: Google Sheets (Test tracking), Teams (Internal team comms)
Job Title: Senior Full Stack Java Developer (AI-Driven Mindset | Java, React, Azure, Kubernetes, Microservices) About the Role We are seeking an experienced Senior Full Stack Java Developer with an AI-first mindset. Someone passionate about building modern applications today while also thinking ahead about how AI, GenAI, and intelligent automation will shape the products of tomorrow. This role involves designing, developing, and deploying high-performance applications using Java (backend) and React (frontend) , with strong expertise in Azure Cloud, Kubernetes, and Microservices . Beyond the tech stack, we want someone eager to embed AI into workflows, explore LLM integration, and drive innovation across our engineering practices. High-performing candidates will have opportunities for performance-based stock options and US work sponsorship (H1B and Green Card pathways) to work with global clients. Key Responsibilities Design, develop, and maintain scalable, resilient, and secure full-stack applications. Build microservices using Java/Spring Boot and modern best practices. Develop rich, interactive, and responsive user interfaces using React.js . Deploy, manage, and scale applications on Azure Cloud using Kubernetes (AKS) . Collaborate with cross-functional teams (Product, DevOps, QA, UX) to deliver end-to-end solutions. Implement CI/CD pipelines for automated builds, testing, and deployments. Ensure application security, performance tuning, and optimization. Mentor junior developers and contribute to technical decision-making. Participate in design reviews, code reviews, and architecture discussions. Stay current with emerging technologies and advocate for best practices. Required Skills & Experience 8+ years of professional software development experience. Strong expertise in Java, Spring Boot, REST APIs, and Microservices . Proven front-end development experience with React.js, Redux, JavaScript (ES6+), HTML5, CSS3 . Hands-on experience deploying and managing applications on Microsoft Azure (App Services, AKS, Azure Functions, Event Hub, etc.). Experience with Kubernetes (preferably AKS) for container orchestration. Proficiency with CI/CD pipelines using tools like Azure DevOps, Jenkins, or GitHub Actions. Strong knowledge of SQL/NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Cosmos DB). Familiarity with Docker, Helm, Terraform, or ARM templates for infrastructure as code. Good understanding of security best practices (OAuth2, JWT, Identity Management). Solid grasp of software architecture, design patterns, and cloud-native principles . Excellent communication, collaboration, and problem-solving skills. Preferred Skills Experience with Event-Driven Architectures (Kafka, RabbitMQ, Service Bus). Knowledge of GraphQL for API development. Familiarity with monitoring & logging tools (Prometheus, Grafana, ELK, App Insights). Previous exposure to Agile/Scrum methodologies. Prior experience leading a team or mentoring developers. Education Bachelors or Masters degree in Computer Science, Engineering, or related field (or equivalent work experience). KPIs / Success Metrics First 90 Days Successfully onboard into the team and development environment. Deliver at least one end-to-end feature (frontend + backend + deployment on Azure). Set up or enhance CI/CD pipelines to improve deployment efficiency. Participate actively in code reviews and architecture discussions . Establish working relationships with cross-functional teams. First 6 Months Lead the development of major modules or microservices . Optimize application performance, reducing response times and resource costs . Increase test coverage and improve code quality metrics. Contribute to cloud migration or scaling initiatives on Azure Kubernetes Services (AKS). Mentor junior developers with measurable improvements in their output. First 12 Months Act as a go-to technical leader for full stack solutions. Successfully deliver multiple high-impact projects deployed to production. Implement or improve monitoring, alerting, and logging frameworks . Drive adoption of best practices across the team (security, DevOps, architecture). Demonstrate measurable business impact through improved system scalability, uptime, and cost efficiency . Growth & Rewards Stock Options : Performance-based equity opportunities tied to project success and business outcomes. Work in the USA : Potential opportunities to travel and work with US clients. Visa Sponsorship : H1B and Green Card sponsorship available for high-performing long-term employees. Opportunity to work on modern cloud-native applications . Professional growth, leadership, and global client exposure.
Job Title: US Bench Sales & Talent Acquisition Lead Job Overview We are seeking an experienced and dynamic US Bench Sales & Talent Acquisition Lead to join our team. The ideal candidate will not only market and place IT consultants (bench candidates) but also source, screen, and recruit suitable candidates for client job requirements. This dual role ensures both effective bench utilization and proactive talent acquisition for new positions. This position offers a unique opportunity to scale from an employee to a leader and grow into a stakeholder in the company . With a profit-sharing model based on sales performance , the right candidate can build a long-term career with both financial and leadership rewards. Roles & Responsibilities Grow and manage the consultant bench by engaging, onboarding, and retaining qualified IT professionals. Successfully place bench consultants by aligning them with client requirements and ensuring long-term stability. Source, screen, and recruit new candidates for client job requirements when no bench candidate is available. Lead, mentor, and manage a team of recruiters to achieve placement and revenue goals. Build and nurture strong relationships with direct clients, prime vendors, and implementation partners. Develop new business opportunities and onboard new vendors/clients. Negotiate contract terms, rates, and placement conditions with clients/vendors. Track consultant pipelines, submissions, interviews, and placements through ATS/CRM. Prepare candidates for interviews and support them through the hiring process. Analyze market demand and align strategies to meet client requirements. Report team performance, placement metrics, and revenue growth to management. Drive team profitability while ensuring high levels of consultant satisfaction. Preferred Candidate Profile Bachelors degree or equivalent experience. 10+ years of proven US IT Bench Sales & Recruiting experience with at least 2+ years in a lead/mentorship role . Strong understanding of US tax terms (W2, C2C, 1099) and visa classifications. Excellent communication, negotiation, and leadership skills. Proven track record of bench placements and direct hires for client requirements. Established vendor/client network preferred. Ability to inspire, lead, and grow a team. Entrepreneurial mindset with a drive to grow into a leadership and ownership role. Proficiency in job portals (Dice, Monster, CareerBuilder), ATS systems, and LinkedIn. Benefits Profit-sharing opportunity based on team sales performance. Growth path from employee leader stakeholder in the company. Fast-track career growth with leadership opportunities.
Job Title: DevOps Engineer Azure Infra Setup About the Role: We’re looking for a highly skilled DevOps Engineer to design, configure, and deploy cloud infrastructure on Microsoft Azure for our AI-based agentic system. This infrastructure will host AI agents, support NoSQL data storage, and provide logging, alerting, and system observability. This is a project-based role where you'll own the setup of scalable, secure, and maintainable infrastructure to support ML models, APIs, and multi-agent orchestration. Key Responsibilities: Azure Cloud Infrastructure Setup Design and provision compute resources (VMs), networking, and storage optimized for AI/ML workloads. NoSQL Deployment & Integration Set up and configure NoSQL databases (e.g., Azure Cosmos DB or MongoDB) for agent data operations. Monitoring & Observability Implement infrastructure monitoring, health checks, and logging (e.g., Azure Monitor, Log Analytics, or Grafana). Security & Access Control Configure secure access using Azure AD, firewalls, and role-based access control (RBAC). Automation & IaC (Infrastructure as Code) Use tools like Terraform , ARM templates , or Bicep to script and automate deployments. CI/CD Integration Support (if required) Assist in setting up pipelines for deployment of AI models or agents. Documentation & Handoff Deliver architecture diagrams, access instructions, and runbooks for long-term maintainability. Required Skills & Qualifications: 3+ years experience in DevOps or cloud infrastructure roles Strong proficiency with Microsoft Azure , including: Azure Virtual Machines (Linux/Windows) Azure Storage (Blob, Table) Azure Monitor / Application Insights Azure Networking (VNet, NSG, Load Balancer) Experience deploying and managing NoSQL databases (Azure Cosmos DB, MongoDB, or equivalent) Strong skills in scripting and automation (Bash, PowerShell, Python) Working knowledge of IaC tools : Terraform, Bicep, or Azure Resource Manager (ARM) templates Familiarity with logging and monitoring tools like Grafana , Prometheus , or ELK Understanding of AI/ML infra needs such as GPU provisioning, data flow, and model hosting Bonus / Preferred Experience: Previous experience supporting AI agent deployments or ML model hosting Experience with containerization (Docker) and orchestration (Kubernetes, AKS) Understanding of CI/CD pipelines using GitHub , Azure DevOps, or Bitbucket Tools You May Use: Microsoft Azure (VMs, Storage, Cosmos DB, Monitor, AD) Terraform / Bicep Bash, PowerShell Azure CLI Grafana / Log Analytics GitHub or Bitbucket (for repo and deployment coordination)