DIGIT is seeking a System Administrator to manage and maintain printers throughout the enterprise for the General Services Administration. The individual will support the management of devices throughout their entire lifecycle. This includes administering, operating, maintaining, and supporting enterprise device management platforms, tools, systems, and related integrations used to manage network-connected devices. There are over 1,400 printers in the GSA environment that are deployed throughout the country. As a leading provider of advanced information technology solutions and professional services to U.S. federal government agencies, is the prime for an $807m task order in support of the General Services Administration (GSA) Office of Digital Infrastructure Technologies (IDT) DIGIT (Digital Innovation for GSA Infrastructure Technologies) task order driving digital transformation and delivering continuous improvement and business value to its customers. The team is comprised of the best-in-class technology partners to leverage forward-leaning technologies and best practices to transform GSA’s IT capabilities and shift offerings to provide a more flexible service delivery model, completing the agency’s shift to a fully digital experience along with its adoption of advanced, emerging technologies such as intelligent automation, artificial intelligence, and machine learning.

Role Description Performacentric is seeking a Machine Learning Engineer with hands-on experience developing and deploying AI applications using Llama 3 8B, Python, and FastAPI. This role will be responsible for building production-grade AI services, optimizing model performance, developing APIs, integrating business systems, and supporting the evolution of Performacentric's AI agent platform. The ideal candidate combines strong software engineering skills with practical machine learning experience and enjoys working in a fast-paced startup environment where they can directly influence product direction and technical architecture. Responsibilities - AI Model Development & Optimization - Deploy, configure, and optimize Llama 3 8B models for production use. - Develop prompt engineering, retrieval, and agentic workflows. - Fine-tune and evaluate LLM performance for business use cases. - Implement Retrieval-Augmented Generation (RAG) architectures. - Optimize inference performance, latency, and infrastructure utilization. - Monitor model quality and continuously improve response accuracy. - Application Development - Build scalable AI applications using Python and FastAPI. - Design and maintain RESTful APIs for AI services. - Develop backend services supporting AI agents and copilots. - Integrate AI solutions with CRM, ERP, communication, and business systems. - Implement authentication, authorization, and API security controls. - Write clean, maintainable, and well-documented code. - Data & Infrastructure - Build and maintain vector database integrations. - Develop data ingestion and preprocessing pipelines. - Support deployment of AI workloads in cloud and self-hosted environments. - Collaborate on model serving, monitoring, logging, and observability. - Assist with infrastructure automation and CI/CD processes. - Collaboration - Work closely with product, engineering, and leadership teams. - Participate in architecture discussions and technical planning. - Contribute to AI solution design for client implementations. - Mentor junior developers and share best practices. Qualifications - 3+ years of professional software engineering experience. - Strong proficiency in Python. - Experience building APIs with FastAPI. - Experience deploying and working with Llama 3 8B or similar open-source LLMs. - Understanding of prompt engineering and LLM optimization techniques. - Experience consuming and developing REST APIs. - Strong understanding of Git-based development workflows. - Familiarity with Linux environments and command-line tools. - Experience troubleshooting and optimizing production applications. Requirements - Understanding of machine learning fundamentals. - Experience evaluating AI model performance. - Familiarity with embeddings, vector search, and RAG architectures. - Knowledge of model inference optimization techniques. - Experience working with structured and unstructured datasets. Preferred Qualifications - Fine-tuning open-source LLMs. - ML Engineering and MLOps practices. - LangChain, LlamaIndex, Haystack, or similar frameworks. - PostgreSQL database administration and optimization. - Vector databases such as pgvector, Chroma, Pinecone, Weaviate, or Qdrant. - Docker and containerized deployments. - Kubernetes orchestration. - Azure AI infrastructure and GPU environments. - CI/CD pipelines and DevOps automation. - Multi-agent AI architectures. - Knowledge graph implementations. - Business intelligence and analytics platforms. Success Metrics - Deploy and optimize production AI workloads. - Improve AI response quality and accuracy. - Reduce inference latency and infrastructure costs. - Expand Performacentric's AI agent platform capabilities. - Deliver reliable AI integrations for customer environments. - Contribute to the development of new AI-powered products and services. Benefits - Opportunity to work on cutting-edge AI and agentic technologies. - Direct influence on product architecture and technical strategy. - Remote-first work environment. - Competitive compensation based on experience. - Professional growth opportunities in one of the fastest-growing areas of software development. - Ability to help shape the future of AI-powered business transformation. How to Apply - Resume/CV - Brief cover letter - GitHub profile (if available) - Portfolio of AI, machine learning, or software development projects - Examples of LLM, FastAPI, or AI agent implementations (preferred)

Role Description Performacentric is seeking a Machine Learning Engineer with hands-on experience developing and deploying AI applications using Llama 3 8B, Python, and FastAPI. This role will be responsible for building production-grade AI services, optimizing model performance, developing APIs, integrating business systems, and supporting the evolution of Performacentric's AI agent platform. The ideal candidate combines strong software engineering skills with practical machine learning experience and enjoys working in a fast-paced startup environment where they can directly influence product direction and technical architecture. Responsibilities - AI Model Development & Optimization - Deploy, configure, and optimize Llama 3 8B models for production use. - Develop prompt engineering, retrieval, and agentic workflows. - Fine-tune and evaluate LLM performance for business use cases. - Implement Retrieval-Augmented Generation (RAG) architectures. - Optimize inference performance, latency, and infrastructure utilization. - Monitor model quality and continuously improve response accuracy. - Application Development - Build scalable AI applications using Python and FastAPI. - Design and maintain RESTful APIs for AI services. - Develop backend services supporting AI agents and copilots. - Integrate AI solutions with CRM, ERP, communication, and business systems. - Implement authentication, authorization, and API security controls. - Write clean, maintainable, and well-documented code. - Data & Infrastructure - Build and maintain vector database integrations. - Develop data ingestion and preprocessing pipelines. - Support deployment of AI workloads in cloud and self-hosted environments. - Collaborate on model serving, monitoring, logging, and observability. - Assist with infrastructure automation and CI/CD processes. - Collaboration - Work closely with product, engineering, and leadership teams. - Participate in architecture discussions and technical planning. - Contribute to AI solution design for client implementations. - Mentor junior developers and share best practices. Qualifications - 3+ years of professional software engineering experience. - Strong proficiency in Python. - Experience building APIs with FastAPI. - Experience deploying and working with Llama 3 8B or similar open-source LLMs. - Understanding of prompt engineering and LLM optimization techniques. - Experience consuming and developing REST APIs. - Strong understanding of Git-based development workflows. - Familiarity with Linux environments and command-line tools. - Experience troubleshooting and optimizing production applications. Machine Learning Knowledge - Understanding of machine learning fundamentals. - Experience evaluating AI model performance. - Familiarity with embeddings, vector search, and RAG architectures. - Knowledge of model inference optimization techniques. - Experience working with structured and unstructured datasets. Preferred Qualifications - Fine-tuning open-source LLMs. - ML Engineering and MLOps practices. - LangChain, LlamaIndex, Haystack, or similar frameworks. - PostgreSQL database administration and optimization. - Vector databases such as pgvector, Chroma, Pinecone, Weaviate, or Qdrant. - Docker and containerized deployments. - Kubernetes orchestration. - Azure AI infrastructure and GPU environments. - CI/CD pipelines and DevOps automation. - Multi-agent AI architectures. - Knowledge graph implementations. - Business intelligence and analytics platforms. Success Metrics - Deploy and optimize production AI workloads. - Improve AI response quality and accuracy. - Reduce inference latency and infrastructure costs. - Expand Performacentric's AI agent platform capabilities. - Deliver reliable AI integrations for customer environments. - Contribute to the development of new AI-powered products and services. Benefits - Opportunity to work on cutting-edge AI and agentic technologies. - Direct influence on product architecture and technical strategy. - Remote-first work environment. - Competitive compensation based on experience. - Professional growth opportunities in one of the fastest-growing areas of software development. - Ability to help shape the future of AI-powered business transformation. How to Apply - Resume/CV - Brief cover letter - GitHub profile (if available) - Portfolio of AI, machine learning, or software development projects - Examples of LLM, FastAPI, or AI agent implementations (preferred)

Role Description As a Principal Site Reliability Engineer, you will set technical and operational direction for reliability across Substrate workloads. You operate at broad scope, influencing architecture, reliability strategy, and engineering practices across teams and organizations. - Lead complex, multi-year initiatives. - Shape how reliability is designed into systems. - Act as a role model for technical excellence, leadership, and Microsoft values—particularly for services operating in regulated, sovereign, or compliance-sensitive environments. Responsibilities - Define and drive reliability strategy, SLO frameworks, and operational best practices across Substrate workloads in highly regulated environments. - Serve as an actively engaged senior on-call engineer (OCE), participating in on-call rotations and leading incident response for Substrate services in regulated environments. - Provide hands-on leadership during the most complex or high-impact incidents, setting technical direction and response strategy. - Drive high-quality post-incident reviews that result in durable, systemic engineering improvements across teams. - Architect and deliver large-scale automation, observability, and self-healing solutions. - Drive architectural decisions and define software engineering standards that make reliability, security, and compliance intrinsic to Substrate services. - Influence service design and engineering decisions across organizational boundaries. - Mentor senior and principal engineers and shape the long-term technical direction of the SRE discipline. - Represent Substrate SRE perspectives with senior leadership and cross-functional partners. Qualifications - Doctorate Degree in Computer Science, Information Technology, or related field AND 2+ years technical experience in software engineering, network engineering, or systems administration - OR Master's Degree in Computer Science, Information Technology, or related field AND 3+ years technical experience in software engineering, network engineering, or systems administration - OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 5+ years technical experience in software engineering, network engineering, or systems administration - OR equivalent experience. Requirements - Security Clearance Requirements: Candidates must be able to meet Microsoft, customer and/or government security screening requirements required for this role. - This role requires access to Microsoft Government cloud environments, including GCC Moderate (GCCM), GCC High (GCCH), and Department of Defense (DoD) environments. - Ability to obtain and maintain a favorably adjudicated Tier 3 (T3) background investigation for access to GCCH and DoD environments. - Ability to meet Criminal Justice Information Services (CJIS) eligibility requirements for access to GCCM environments. - For manager-level roles, a Tier 5 (T5) background investigation is preferred. - Candidates may be considered without currently holding these background investigations, provided they are eligible for and able to successfully obtain them. - Candidates may begin work while required background investigations are in progress; however, failure to obtain or maintain the appropriate clearance and/or customer screening requirements may result in employment action up to and including termination. - This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter. Preferred Qualifications - Doctorate Degree in Computer Science, Information Technology, or related field AND 5+ years technical experience in software engineering, network engineering, or systems administration - OR Master's Degree in Computer Science, Information Technology, or related field AND 8+ years technical experience in software engineering, network engineering, or systems administration - OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 12+ years technical experience in software engineering, network engineering, or systems administration - OR equivalent experience. - 7+ years technical experience working with large-scale cloud or distributed systems. - 3+ years people management experience. - Experience operating or supporting services in regulated, sovereign, or compliance-sensitive environments. Benefits - The typical base pay range for this role across the U.S. is USD $142,800.00 - $274,800.00 per year. - There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000.00 - $304,200.00 per year. - Certain roles may be eligible for benefits and other compensation.