CodeRoad

AI Engineer

• Lead the architectural design and deployment of scalable, resilient autonomous AI systems and multi-agent decision engines
• Engineer and maintain high-throughput vector retrieval pipelines for RAG and semantic search
• Establish real-time AI evaluation and observability pipelines to monitor model health, latency, drift, and bias
• Develop interactive AI user interfaces using Streamlit, Chainlit, and React concepts
• Integrate safety guardrails, MCP integrations, structured function calling, and regulatory compliance standards
• Establish foundational AI engineering patterns
• Mentor junior and mid-level software engineers
• Collaborate across MLOps, cloud, and product teams

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Senior Agentic AI Engineer

Backend Engineer

• Engage directly with business stakeholders to translate open-ended business needs into concrete technical features
• Define requirements and edge cases from ground zero
• Act as a proactive technical advisor, challenging assumptions and proposing improvements
• Articulate architectural trade-offs to technical leadership and non-technical stakeholders
• Design, deploy, and maintain resilient microservices, RESTful/GraphQL APIs, and relational database schemas
• Optimize database queries, Redis caching strategies, and Apache Kafka event-driven messaging pipelines
• Support high-volume real-time data synchronization and e-commerce workflows
• Integrate AI coding tools into daily development workflows
• Own features end-to-end from discovery through architecture, implementation, testing, deployment, and operational health

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Lead Java Consultant

DevOps Engineer

• Provision and manage end-to-end Azure infrastructure using Terraform or Bicep across development, staging, and production environments
• Build and maintain deployment pipelines through Azure DevOps or GitHub Actions
• Author comprehensive operational runbooks for repeatable platform delivery
• Provision, back up, and secure relational databases, object storage, and vector databases
• Implement fine-grained access control for core platform data stores
• Enforce least-privilege Azure RBAC with Managed Identities
• Optimize secrets management with Azure Key Vault
• Establish secure API key lifecycles for LLM integrations
• Containerize and deploy backend Python services, custom dashboards, and AI orchestration/observability tooling
• Support agentic workflows and prompt evaluation
• Enforce network security through private endpoints and virtual networks
• Integrate automated CI/CD security scanning, Microsoft Defender for Cloud, and Azure Monitor
• Collaborate with distributed U.S. and nearshore engineering teams

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Mid Azure DevOps Engineer

Full-stack Engineer

• Contribute to migrating existing Delphi and Zoho applications into modern web architectures
• Build and enhance frontend features using React and backend services using Python and Django
• Work across frontend and backend components to deliver reliable, end-to-end functionality
• Support and enhance Zoho CRM, Creator, Desk, Books, and Analytics applications
• Implement custom functions, workflows, and business process automation using Deluge where applicable
• Investigate, troubleshoot, and enhance existing Delphi (Object Pascal) applications
• Develop and consume RESTful APIs, webhooks, and integrations across legacy applications, modern web services, databases, and third-party platforms
• Write and optimize SQL queries; support data migration and validation; maintain data integrity
• Use AI-assisted coding and development tools to accelerate implementation, understand legacy codebases, generate and validate tests, troubleshoot issues, and improve workflows
• Collaborate with senior engineers, product teams, and business stakeholders
• Translate requirements into well-tested, documented solutions
• Follow Git, code review, and CI/CD practices while owning assigned tasks

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Fullstack Developer, Python/React

Machine Learning Engineer

• Design, deploy, and manage scalable, production-grade deployment pipelines for autonomous AI models and multi-agent workflows using LangGraph, CrewAI, and n8n
• Build and maintain resilient multi-cloud AI infrastructure on GCP or AWS
• Establish automated CI/CD and Continuous Training pipelines using Git, Docker, and Kubernetes
• Implement observability frameworks using Langtrace, AgentOps, Prometheus, CloudWatch, and Grafana
• Track model drift, latency, and autonomous decision accuracy in real time
• Implement security architectures including IAM, data encryption, prompt injection defense, and automated GDPR safety guardrails
• Establish data quality standards, version control, and ethical AI safeguards
• Collaborate with cross-functional AI teams across the operational lifecycle

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Machine Learning Operations Engineer – MLOps

• Design and deploy hierarchical multi-agent workflows using LangGraph
• Transition legacy single-prompt applications into modular, autonomous systems
• Build domain-specific sub-agents with dynamic, on-demand context-loading mechanisms
• Optimize operational security, performance, latency, and token costs
• Integrate scalable tool-calling capabilities and standardized connectors using the Model Context Protocol (MCP)
• Manage and configure LangFuse for LLM reasoning traces, performance metrics, and cost allocation
• Create regression testing suites and automated evaluation harnesses using PromptFlow or PromptFoo
• Collaborate with security leads to integrate access controls and OWASP Top 10-aligned guardrail frameworks for LLM applications

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Data Engineer

• Develop, optimize, and maintain reliable ETL/ELT pipelines to ingest, transform, validate, and load transactional data into Microsoft SQL Server and PostgreSQL.
• Contribute to data ingestion and preparation workflows supporting AI applications, including document processing, metadata management, chunking, and embedding generation for RAG systems.
• Use modern AI tools to accelerate code development, improve data transformation workflows, troubleshoot issues, and improve data engineering efficiency.
• Develop, schedule, and monitor data processing jobs using Airflow, Azure Data Factory, and SQL Server Agent.
• Investigate pipeline failures and implement data quality checks.
• Write and optimize SQL queries, contribute to data modeling, and improve database and analytical data structure performance.
• Collaborate with Senior Data Engineers, data scientists, product owners, and IT Operations to resolve production issues and implement improvements.
• Maintain documentation and follow Git, Bitbucket, and Jira workflows.

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• Identify, investigate, and troubleshoot missing, duplicate, inconsistent, or unexpected data in operational systems
• Use SQL queries to trace data issues, identify root causes, and help implement resolutions
• Monitor data processing workflows, scheduled jobs, and pipeline executions
• Investigate pipeline failures, identify bottlenecks, and coordinate troubleshooting to maintain reliable data flows
• Troubleshoot data discrepancies, failed refreshes, and issues affecting reporting tools and dashboards
• Perform approved data corrections and operational changes while following procedures, documentation, and data integrity standards
• Streamline recurring investigations and reduce manual effort through scripting, automation, and AI-assisted tools
• Experiment with AI solutions to accelerate troubleshooting and improve operational workflows
• Work within a three-person infrastructure team to resolve data-related operational issues
• Track incidents and resolutions through ticketing systems
• Collaborate with developers, IT Operations, and business stakeholders

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Data Engineer – Support Project L2

• Design and implement modern semantic models, including facts, dimensions, and metrics stores using tools like dbt, for AI querying and analytical consumption
• Build curated, high-performance analytical views and production ELT/ETL pipelines
• Optimize database query performance, indexing, and schema definitions through systematic query log analysis
• Implement data governance frameworks including RBAC, RLS, and dynamic data masking for PII protection
• Establish data quality, pipeline monitoring, and anonymization standards across analytical layers
• Collaborate with cross-functional AI engineering teams to align data structures with RAG architectures and Agentic AI workflows

• Develop production-grade AI agents, state management mechanisms, and multi-agent workflows
• Connect AI agents to enterprise tools, APIs, and external platforms via Model Context Protocol and structured function calling
• Implement and maintain high-performance RAG pipelines using vector databases, semantic search, chunking, and grounding strategies
• Execute automated evaluations, regression testing, task-success metrics, and safety guardrails
• Defend against prompt injection and sensitive-data exposure
• Deploy LLM applications on AWS using Amazon Bedrock
• Use observability platforms such as LangSmith, Langtrace, or AgentOps to troubleshoot failures and optimize performance
• Write clean, maintainable, and testable Python code
• Maintain comprehensive technical documentation
• Collaborate with senior engineers, MLOps, and product teams

Open roles

Senior Agentic AI Engineer

Lead Java Consultant

Mid Azure DevOps Engineer

Fullstack Developer, Python/React

Machine Learning Operations Engineer – MLOps

AI Engineer

Data Engineer

Data Engineer – Support Project L2

Data Engineer

AI Engineer