Design and implement intelligent agent architectures, develop multi-agent systems for communication and collaboration, and build advanced pipelines integrating unstructured data with knowledge graphs to enhance AI reasoning and performance.

AI Engineer Locations: Fort Belvoir, VA Reston, VA Hybrid time type Full time job requisition id R0239413 Job Description: AI Engineer The Opportunity: As an experienced engineer, you know that artificial intelligence is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques makes you an integral part of delivering a customer-focused solution. We need your technical knowledge and desire to problem-solve to support an enterprise-wide application and data capability. As an artificial intelligence engineer on our national security team, you'll train, test, deploy, and maintain models that learn from data. In this role, you'll own and define the direction of critical solutions by applying best-fit ML algorithms and technologies. You'll be part of a large community of artificial intelligence engineers across the company and collaborate with data engineers, data scientists, solutions architects, and MLOps engineers to deliver world class solutions, including processing data and information at a massive scale. Your skills and extensive technical expertise will guide clients as they navigate the landscape of AI/ML algorithms, tools, and frameworks. Work with us to solve real-world challenges and define AI strategy for our national security customers. Join us. The world can't wait. You Have: - 3+ years of experience in software engineering, machine learning engineering, or AI systems development using Python - Experience building, deploying, and maintaining LLM-powered systems and complex data pipelines in production environments - Experience implementing advanced prompting strategies, LangChain or LlamaIndex AI orchestration frameworks, vLLM or TGI LLM inference engines, and tool calling or agentic workflows - Experience integrating AI components, including embeddings, vector databases such as LanceDB, pgvector, or Elasticsearch, and Retrieval-Augmented Generation (RAG) pipelines - Ability to translate early-stage prototypes into robust, scalable production systems - Ability to take ownership of system performance, continuously optimizing for latency, throughput, and high reliability - TS/SCI clearance - Bachelor's degree Nice If You Have: - Experience collaborating with MLOps, DevOps, and infrastructure engineers to ensure seamless model deployment and infrastructure scaling - Experience working with AWS, Azure, or GCP cloud platforms and containerization technologies, such as Docker or Kubernetes - Experience with underlying model architectures, inference optimization techniques, or fine-tuning open-source models - Experience with distributed systems, streaming data such as Kafka, or integrating AI services into larger enterprise microservice architecture - Security+ Certification Clearance: Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance is required. Compensation At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen's benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page. Salary at Booz Allen is determined by various factors, including but not limited to location, the individual's particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $99,000.00 to $225,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen's total compensation package for employees. This posting will close within 90 days from the Posting Date. Identity Statement As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud. Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided. Work Model Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings. - Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility. - Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility. - Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.

Role Description The Azure AI Engineer is responsible for the end-to-end implementation and deployment of enterprise AI solutions on the Azure Stack. You will take ownership of building, integrating, and operationalizing AI workloads using Azure AI Foundry, Azure Data Lake, and the broader Microsoft AI ecosystem — including the design and enforcement of guardrails for responsible, secure, and compliant AI. This is a hands-on engineering role focused on delivery: turning architectural designs into production-ready AI services, owning the deployment lifecycle, and ensuring solutions are robust, observable, and aligned with enterprise security and governance standards. Responsibilities: - AI Solution Implementation - Implement AI solutions on Azure AI Foundry — including agent design, model selection, prompt flows, evaluation pipelines, and deployment of base and fine-tuned models. - Build retrieval-augmented generation (RAG) pipelines using Azure AI Search, Azure OpenAI, and vector stores; consume curated data from upstream data platforms. - Deploy models and AI endpoints to Azure Machine Learning, Azure AI Foundry, and Azure Container Apps; manage endpoint scaling, versioning, and traffic routing. - Integrate AI services with downstream applications via REST APIs, Azure API Management, and Function Apps. - AI Guardrails & Responsible AI - Implement input/output guardrails using Azure AI Content Safety, Prompt Shields, and groundedness checks; configure jailbreak, PII, and harmful-content filters. - Build evaluation pipelines for safety, groundedness, relevance, and bias using Azure AI Foundry evaluations; embed Responsible AI checks into the deployment workflow. - Work within enterprise security patterns — Managed Identity, Key Vault, private endpoints, and RBAC — for all AI services. - Deployment & MLOps - Build and maintain CI/CD pipelines (Azure DevOps or GitHub Actions) for prompt flows, model evaluation, and endpoint deployment; implement model registry and promotion gates. - Instrument AI workloads with Azure Monitor, Application Insights, and Log Analytics; set up dashboards for token usage, latency, cost, and guardrail violations. - Contribute to Terraform or Bicep modules for AI Foundry, AML, and AI Search resources (working alongside platform engineering for foundational infra). Qualifications - Hands-on experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and Azure AI Content Safety. - Experience building RAG, agentic, or prompt-flow solutions; familiarity with frameworks such as LangChain, Semantic Kernel, or LlamaIndex. - Strong Python skills; comfortable with REST APIs, async patterns, and SDK-based integrations (Azure SDK, OpenAI SDK). - Working familiarity with Azure Data Lake Storage Gen2 and Delta tables — sufficient to consume curated data for AI use cases (deep Databricks/PySpark engineering not required). - Hands-on with Azure DevOps or GitHub Actions for CI/CD; Git-based workflows. - Comfortable contributing to existing Terraform or Bicep modules. - Working knowledge of Docker and Azure Container Apps for deploying AI services. - Azure Monitor, Application Insights, and Log Analytics for AI workload telemetry. - Practical experience implementing content safety, prompt shields, or groundedness checks in production AI systems. - Familiarity with offline and online evaluation methods for LLM applications (groundedness, relevance, safety). - Understanding of Responsible AI principles and regional compliance considerations relevant to UAE / regulated industries. - Works closely with architects, data engineers, and security teams; communicates clearly across technical and non-technical audiences. - Produces clear technical design documents and deployment runbooks. Requirements - 3–5 years of hands-on engineering experience, including at least 2 years building AI or ML solutions on Azure. - Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience). - Microsoft Certified: Azure AI Engineer Associate (AI-102) (preferred). Location - Remote, aligned to GST business hours. Engagement - Full-time.

Role Description We’re looking for an AI Engineer to help build the data and model foundations that make Modern Relay’s platform reliable in production. You’ll work across data pipelines, model development, and ML infrastructure, turning messy signals into structured knowledge and high-quality model behavior. This role is ideal for someone who enjoys shipping end-to-end systems, from schema design and data infrastructure to training/evaluating models and improving them with feedback loops. What You’ll Do - Design and build data pipelines that ingest, clean, and transform product and customer data into high-signal training and evaluation datasets - Own data infrastructure decisions (storage, orchestration, lineage, observability) to ensure reliability, scalability, and fast iteration - Develop and improve ML/AI systems that power agent's behavior in task-solving, including retrieval, ranking, classification, and structured extraction - Create and maintain schemas for agent memory, tool outputs, and conversation artifacts to make downstream modeling and analytics consistent - Build evaluation harnesses and metrics to measure model quality, regressions, and real-world performance (offline + online) - Work with knowledge representations (e.g., knowledge graphs) to connect entities, events, and business context for better reasoning and retrieval - Partner closely with Product and Engineering to integrate models into production workflows with clear SLAs and monitoring - Continuously improve feedback loops: labeling strategies, active learning, error analysis, and dataset/version management What Success Looks Like - Data pipelines and datasets are trustworthy, well-instrumented, and easy to iterate on as product needs evolve - Model performance improves measurably over time with clear evaluation methodology and fast debugging cycles - Agent outputs become more consistent and structured through strong schema design and robust post-processing/validation - Knowledge and retrieval systems reduce hallucinations and increase task completion rates in real customer workflows - Cross-functional teams can confidently ship AI improvements because quality, monitoring, and rollback paths are in place Qualifications - 0–6 years of experience in AI/ML engineering, data engineering, or a closely related role (we’re open to exceptional new grads with strong projects) - Strong fundamentals in data engineering: pipelines, data modeling, schema design, and data quality practices - Experience building or operating ML systems in production (training, evaluation, deployment, monitoring) or strong evidence you can ramp quickly - Comfort working across the stack: from raw data and infrastructure to model behavior and product integration - Familiarity with modern ML platforms and tooling (experiment tracking, dataset/versioning, orchestration, feature/data stores, model serving) - Understanding of information theory concepts (e.g., entropy, mutual information) and how they relate to signal, compression, and evaluation - Experience with knowledge graphs or structured knowledge representations is a plus - High ownership and a bias toward shipping: you can take ambiguous problems, propose a plan, and execute Key Skills - Data pipelines - Data engineering and data infrastructure - AI / artificial intelligence - Machine learning platforms and production ML - Model development, evaluation, and monitoring - Schema design and structured data systems - Knowledge graphs and information retrieval - Information theory fundamentals Why This Role - Build core AI infrastructure that directly impacts product reliability and customer outcomes - Work on real-world agent coordination problems where data quality, structure, and evaluation matter as much as models - High autonomy and ownership in a fast-moving team shipping at the frontier of applied AI - A chance to define how Modern Relay’s agents learn from data and improve over time