Jobs at CodeRoad

Senior QA Engineer responsible for functional, exploratory, integration, API, backend, and database testing across distributed logistics systems. The role builds automation for REST and gRPC microservices, validates data flows with SQL, investigates production issues, documents platform gaps, and integrates quality processes into CI/CD pipelines. It is a fully remote contractor position requiring strong technical communication and collaboration with U.S. engineering and operations teams.

Design, build, deploy, and scale production-ready autonomous AI agents and multi-agent workflows on AWS. Develop RAG pipelines, MCP integrations, vector database solutions, evaluation frameworks, observability, security controls, and human-in-the-loop guardrails. Lead cloud architecture, mentor nearshore engineering teams, optimize LLM application reliability, and communicate technical architecture to stakeholders.

Design, deploy, monitor, and optimize scalable MLOps pipelines and agentic AI systems in production. Manage cloud infrastructure, CI/CD and continuous training workflows, model monitoring, security, governance, compliance, and performance optimization. Collaborate with researchers, data scientists, engineers, and product teams to deliver reliable, ethical autonomous AI solutions.

Build and deploy production-grade agentic AI applications, including multi-agent workflows, RAG pipelines, enterprise integrations, evaluation systems, security guardrails, and LLM observability. Develop maintainable Python code on AWS using Amazon Bedrock and collaborate with MLOps, product, and engineering teams. The role is fully remote for contractors located in Latin America and requires advanced English proficiency for U.S. client collaboration.

Designs and modernizes secure, scalable enterprise applications using C#, .NET, Blazor, and SQL Server. Leads migrations, performance optimization, architecture, technical standards, and unit-tested development in Agile/Scrum teams. Collaborates with architects, product owners, and QA while evaluating AI-assisted development tools. Requires strong Blazor, database, web standards, problem-solving, architectural decision-making, and advanced English skills.

Leads a nine-month migration from legacy Salesforce environments to ServiceNow. Audits and inventories more than 1,000 dashboards, reports, data models, and dependencies; defines the re-platforming roadmap; consolidates or retires analytics assets; supports ServiceNow cross-training; and coordinates with client leadership to preserve data integrity and reporting continuity.

Develop and deploy predictive machine learning models for anomaly detection, forecasting, rate modeling, and invoice classification. Apply NLP, embeddings, and LLMs to supply chain documents and contracts. Build scalable data pipelines, monitoring systems, and retraining workflows; analyze transactional data; query databases with SQL; create visualizations and executive presentations; and maintain data quality, governance, security, and model performance.

Build and scale full-stack web applications using Python, Django, JavaScript, and modern frontend frameworks. Lead legacy-system modernization, data migrations, API integrations, and production cutovers. Develop RESTful services, optimize SQL across MS SQL Server and PostgreSQL, and automate CI/CD pipelines. Mentor developers, support Agile delivery, collaborate with business users, and maintain high-quality, stable production systems in a remote client-facing environment.

Investigate and resolve enterprise data incidents using SQL and MS SQL Server; monitor Airflow, Azure Data Factory, and SQL Agent pipelines; troubleshoot Tableau dashboards and data warehouses; perform controlled data corrections; automate investigations with Python or PowerShell; and coordinate incident resolution through Jira Service Management with engineering, IT, and business teams.

Develop and modernize Delphi legacy applications while building full-stack web applications and backend services with Python/Django or C#/.NET. Create Zoho solutions using Deluge, workflows, APIs, webhooks, and integrations. Manage SQL Server data, optimize queries, maintain data integrity, and collaborate with product teams and business stakeholders. Apply testing, documentation, Git, and CI/CD practices in a fully remote contractor role.

Build and maintain high-volume ETL/ELT pipelines using Python, SQL, Airflow, Azure Data Factory, and SQL Server/PostgreSQL. Design data ingestion, metadata, chunking, and embedding pipelines for AI and RAG systems. Optimize queries, dimensional models, warehouses, and lakehouses while enforcing data quality and governance. Collaborate with product owners, data scientists, and IT Operations to resolve production issues and maintain Git, Jira, and CI/CD standards.

Build semantic models, analytical views, and production ETL/ELT pipelines; optimize SQL queries and schemas; implement governance, security, data quality, monitoring, and anonymization; and support AI, RAG, and agentic AI workflows. The role requires strong production data engineering experience, dimensional modeling, dbt, database security, query tuning, and advanced English for direct client collaboration.

Design and deploy multi-agent AI systems using LangGraph, optimize token usage and latency, integrate MCP-based tooling, configure LangFuse observability, build automated evaluation harnesses, and implement OWASP-aligned security guardrails. The role requires backend software engineering expertise in Python, production experience with agentic AI and LLM workflows, familiarity with major foundation models, Docker, Azure, and strong English communication.

Owns the strategy, backlog, delivery, quality evaluation, and compliance alignment for enterprise AI products, including LLM applications, AI agents, and RAG implementations. Leads cross-functional teams, defines acceptance criteria and evaluation benchmarks, manages stakeholder workshops, tracks project risks and cloud costs, and ensures secure, scalable production releases.

Own and manage Azure infrastructure across development, staging, and production using infrastructure as code. Build CI/CD pipelines, containerize Python and AI services, secure data platforms, manage secrets and identities, enforce network isolation, and implement monitoring and security scanning. Collaborate with AI, data, and backend engineering teams to deliver resilient cloud infrastructure for LLM-based and agentic applications.

Owns the architecture and technical health of mission-critical .NET backends. Leads development of scalable APIs, manages SQL database design and performance, modernizes workflows, and addresses technical debt. Provides code reviews, mentorship, documentation, and stakeholder communication while supporting reliable delivery across diverse codebases. Requires advanced C#, .NET, ASP.NET Web API, SQL, cloud, DevOps, and distributed architecture expertise in a client-facing nearshore or offshore environment.

Lead the architecture, development, evaluation, and operation of enterprise autonomous AI agents integrated with Salesforce. Build stateful multi-agent workflows using LangGraph, Claude, and MCP; implement evaluation, observability, security, and PII protection; optimize model routing and performance; and establish engineering standards, API contracts, and architecture roadmaps. The role requires extensive software engineering and production LLM experience, strong debugging and stakeholder communication skills, and advanced English proficiency.

Lead enterprise application modernization and develop scalable web applications using C#, .NET, Blazor, and SQL Server. Responsibilities include architectural refactoring, migration planning, performance optimization, technical strategy, code quality standards, Scrum collaboration, technology evaluation, backend optimization, RESTful services, and AI-assisted development.

Design, architect, and deploy autonomous agentic AI systems using multi-agent frameworks, vector databases, monitoring tools, and cloud platforms. Lead technical architecture, mentor developers, build user interfaces, optimize production performance, and implement ethical AI safeguards. Collaborate with researchers, MLOps engineers, data scientists, and product teams while maintaining high standards for code quality, documentation, scalability, transparency, and reliability.

Designs and evolves scalable data pipelines, warehouses, lakes, and streaming architectures. Builds ETL/ELT processes, ensures data quality and security, optimizes database performance, and translates complex requirements into technical solutions. Collaborates with data scientists and stakeholders on modeling while mentoring junior engineers. The role requires modern data stack, cloud, programming, data modeling, communication, and client-facing nearshore or offshore experience.