Highlights
Hands-on experience building and operating MCP servers in production, including tool registration, versioning, and hosting on Azure Functions or equivalent serverless infrastructure. • Deep experience building on top of LLMs in production: tool-calling, RAG, multi-step reasoning, multi-model routing, and context window management.
Numbers & Facts
| Location | Washington, DC |
| Industry | Staffing/Employment Agencies |
| Company Size | 500 to 999 employees |
| Year Founded | 2000 |
| Website | https://conv.com/ |
Description
What You Will Own
• Backend Infrastructure
• Own the end-to-end backend infrastructure on Microsoft Azure: Azure Functions, Azure API Management, Azure Container Apps, and Azure OpenAI Service
• Own the data layer: storage, retrieval pipelines, vector databases, and document indexing that power our internal knowledge search
• Own authentication and identity integration, including Entra ID and application-level access control
• Implement and maintain infrastructure as code for all environments; no manual snowflakes
• Own CI/CD pipelines, deployment automation, and release processes including canary and gradual rollouts
• Own the basics that are non-negotiable on any production system: monitoring, alerting, logging, distributed tracing, SLOs, and incident response runbooks
• Manage secrets, API keys, and credential rotation across all integrations with external providers
• Own cost and token economics across all LLM providers; track spend, set budgets, build guardrails, and optimize for cost-per-query without sacrificing quality
Agentic AI & Protocol Infrastructure
• Own the backend implementation of MCP, including MCP server hosting, tool registration, versioning, and lifecycle management on Azure
• Implement and evolve A2A communication patterns, enabling GRACE agents to interoperate with each other and with external agent systems
• Design and maintain LLM orchestration, routing, and multi-model switching infrastructure across OpenAI GPT, Anthropic Claude, and Google Gemini families
• Build and operate RAG pipelines: document ingestion, chunking, embedding, and semantic search
• Implement robust fallback, retry, circuit-breaker, and graceful degradation patterns for all AI service dependencies
• Own tool-calling infrastructure: registration, execution, error handling, and observability for all tools
Observability & Production Quality
• Build and maintain end-to-end observability for agentic workflows: latency, throughput, error rates, token usage, and LLM quality metrics
• Implement LLM evaluation pipelines including safety checks, regression monitoring, and grounding assessment
• Define and enforce system-level SLOs for availability, response time, and tool call reliability
• Own alerting and on-call runbooks; be the person who knows what broke and why
Engineering Excellence & Team
• Establish and improve coding standards, design review processes, and testing practices
• Communicate technical decisions clearly, in writing and in conversation, to both engineers and non-engineers
• Mentor and unblock other engineers with a bias toward ownership and speed
• Work backward from the user: understand the problem being solved before proposing a solution
• Ensure strong privacy, security, and compliance in all systems, integrations, and data handling
Basic Qualifications
• Bachelor's or Master's in Computer Science, Software Engineering, or related field, or equivalent practical experience
• 7+ years of professional software engineering experience building and operating production systems
• Proven experience in high-velocity environments where you owned and shipped complex products end-to-end
• Strong proficiency in Python and at least one other backend language; familiarity with modern backend frameworks and async patterns
• Solid understanding of distributed systems, APIs, data pipelines, and software design patterns
• Hands-on experience on Microsoft Azure: Azure Functions, API Management, Container Apps, and Azure OpenAI Service
• Experience with containerization, CI/CD, and infrastructure as code
• Strong understanding of authentication and identity systems (OAuth2, OIDC, Azure Entra ID or equivalent)
• Demonstrated ability to own production systems: you have been on-call, debugged incidents, and written the postmortem
Clear, direct communicator who gives and receives feedback well and makes the people around them better
Preferred Qualifications
• Hands-on experience building and operating MCP servers in production, including tool registration, versioning, and hosting on Azure Functions or equivalent serverless infrastructure
• Experience implementing A2A communication patterns and multi-agent orchestration frameworks
• Deep experience building on top of LLMs in production: tool-calling, RAG, multi-step reasoning, multi-model routing, and context window management
• Strong intuition for token economics: cost-per-query, context budgets, and prompt efficiency as first-class engineering concerns
• Experience managing multi-provider LLM integrations including rate limits, fallback routing, and API versioning
• Background in security-conscious engineering in regulated or government environments
• Track record in startup or early-stage environments: 0-to-1 product building, comfort with ambiguity, high sense of urgency
• Experience in big tech building customer-facing platforms or developer infrastructure at scale
• Familiarity with vector databases, embedding pipelines, and semantic search infrastructure
• Backend Infrastructure
• Own the end-to-end backend infrastructure on Microsoft Azure: Azure Functions, Azure API Management, Azure Container Apps, and Azure OpenAI Service
• Own the data layer: storage, retrieval pipelines, vector databases, and document indexing that power our internal knowledge search
• Own authentication and identity integration, including Entra ID and application-level access control
• Implement and maintain infrastructure as code for all environments; no manual snowflakes
• Own CI/CD pipelines, deployment automation, and release processes including canary and gradual rollouts
• Own the basics that are non-negotiable on any production system: monitoring, alerting, logging, distributed tracing, SLOs, and incident response runbooks
• Manage secrets, API keys, and credential rotation across all integrations with external providers
• Own cost and token economics across all LLM providers; track spend, set budgets, build guardrails, and optimize for cost-per-query without sacrificing quality
Agentic AI & Protocol Infrastructure
• Own the backend implementation of MCP, including MCP server hosting, tool registration, versioning, and lifecycle management on Azure
• Implement and evolve A2A communication patterns, enabling GRACE agents to interoperate with each other and with external agent systems
• Design and maintain LLM orchestration, routing, and multi-model switching infrastructure across OpenAI GPT, Anthropic Claude, and Google Gemini families
• Build and operate RAG pipelines: document ingestion, chunking, embedding, and semantic search
• Implement robust fallback, retry, circuit-breaker, and graceful degradation patterns for all AI service dependencies
• Own tool-calling infrastructure: registration, execution, error handling, and observability for all tools
Observability & Production Quality
• Build and maintain end-to-end observability for agentic workflows: latency, throughput, error rates, token usage, and LLM quality metrics
• Implement LLM evaluation pipelines including safety checks, regression monitoring, and grounding assessment
• Define and enforce system-level SLOs for availability, response time, and tool call reliability
• Own alerting and on-call runbooks; be the person who knows what broke and why
Engineering Excellence & Team
• Establish and improve coding standards, design review processes, and testing practices
• Communicate technical decisions clearly, in writing and in conversation, to both engineers and non-engineers
• Mentor and unblock other engineers with a bias toward ownership and speed
• Work backward from the user: understand the problem being solved before proposing a solution
• Ensure strong privacy, security, and compliance in all systems, integrations, and data handling
Basic Qualifications
• Bachelor's or Master's in Computer Science, Software Engineering, or related field, or equivalent practical experience
• 7+ years of professional software engineering experience building and operating production systems
• Proven experience in high-velocity environments where you owned and shipped complex products end-to-end
• Strong proficiency in Python and at least one other backend language; familiarity with modern backend frameworks and async patterns
• Solid understanding of distributed systems, APIs, data pipelines, and software design patterns
• Hands-on experience on Microsoft Azure: Azure Functions, API Management, Container Apps, and Azure OpenAI Service
• Experience with containerization, CI/CD, and infrastructure as code
• Strong understanding of authentication and identity systems (OAuth2, OIDC, Azure Entra ID or equivalent)
• Demonstrated ability to own production systems: you have been on-call, debugged incidents, and written the postmortem
Clear, direct communicator who gives and receives feedback well and makes the people around them better
Preferred Qualifications
• Hands-on experience building and operating MCP servers in production, including tool registration, versioning, and hosting on Azure Functions or equivalent serverless infrastructure
• Experience implementing A2A communication patterns and multi-agent orchestration frameworks
• Deep experience building on top of LLMs in production: tool-calling, RAG, multi-step reasoning, multi-model routing, and context window management
• Strong intuition for token economics: cost-per-query, context budgets, and prompt efficiency as first-class engineering concerns
• Experience managing multi-provider LLM integrations including rate limits, fallback routing, and API versioning
• Background in security-conscious engineering in regulated or government environments
• Track record in startup or early-stage environments: 0-to-1 product building, comfort with ambiguity, high sense of urgency
• Experience in big tech building customer-facing platforms or developer infrastructure at scale
• Familiarity with vector databases, embedding pipelines, and semantic search infrastructure
About Company
"An Opportunity to Help"
It started on day one. Convergenz, LLC began in July 2000, at what was fast evolving into the “Dot Com bust” and a rapid rise in unemployment. Our own customers were getting laid off en masse and sending us their resumes, desperate for work. Despite the recession that followed, little by little, we were able to successfully help find people opportunities; and as a company survive this difficult time in our economy. Over ten years later, our mission of connecting people with jobs has never seemed more important.
By the nature of the Staffing Industry, Convergenz is in a unique position to help, and positively affect peoples’ lives. For over a decade we have been able to assist professionals through historically turbulent times in the American economy. From the Dot-Com bubble, through the post 9/11 wave of layoffs, and the “Great Recession” of 2008—we have been able to offer help and resources to people who have needed access to new career opportunities. Whether it was making a major career change, or simply needing to be placed on a contract assignment to get through “right now”—we hope that we are able to help our consultants and clients through these times of uncertainty.
When we think of what sets us apart from our competition, we immediately come back to the fact that we have big company success but do it with a small company touch. Hands on, no red-tape, responsive to peoples’ needs, inquiries, and requests. In other words—Provide Great Service All the Time. When we founded Convergenz, we set out to build a firm that we would want to work for. Be a successful business – but do it the right way, in the boardroom and in the community.
Every day is an exercise in self-reflection —we look to get better, to learn, and grow. We set out to make a positive difference every day, and take pride in the results.
It started on day one. Convergenz, LLC began in July 2000, at what was fast evolving into the “Dot Com bust” and a rapid rise in unemployment. Our own customers were getting laid off en masse and sending us their resumes, desperate for work. Despite the recession that followed, little by little, we were able to successfully help find people opportunities; and as a company survive this difficult time in our economy. Over ten years later, our mission of connecting people with jobs has never seemed more important.
By the nature of the Staffing Industry, Convergenz is in a unique position to help, and positively affect peoples’ lives. For over a decade we have been able to assist professionals through historically turbulent times in the American economy. From the Dot-Com bubble, through the post 9/11 wave of layoffs, and the “Great Recession” of 2008—we have been able to offer help and resources to people who have needed access to new career opportunities. Whether it was making a major career change, or simply needing to be placed on a contract assignment to get through “right now”—we hope that we are able to help our consultants and clients through these times of uncertainty.
When we think of what sets us apart from our competition, we immediately come back to the fact that we have big company success but do it with a small company touch. Hands on, no red-tape, responsive to peoples’ needs, inquiries, and requests. In other words—Provide Great Service All the Time. When we founded Convergenz, we set out to build a firm that we would want to work for. Be a successful business – but do it the right way, in the boardroom and in the community.
Every day is an exercise in self-reflection —we look to get better, to learn, and grow. We set out to make a positive difference every day, and take pride in the results.
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