• Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference. • Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies. • Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. • Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. • Apply machine learning design patterns to build modular, reusable, and production-ready models. • Collaborate with data engineers to develop high-performance data pipelines for training and inference. • Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. • Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.

MLOps Engineer Location: Arlington United States Full time Job Description: The Opportunity: As an experienced MLOps engineer, you know that machine learning 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 Agentic AI platform development. As a MLOps engineer on our Air Power team, you'll train, test, deploy, and maintain models that learn from data. In this role, you'll lead the direction of critical solutions by applying best-fit ML algorithms and introducing leading-edge technologies. You'll share your knowledge with a large community of machine learning engineers across the company and collaborate with AI software engineers, DevSecOps, and Data Engineers to deliver world class solutions to deliver a working Agentic AI Platform. Your skills and extensive technical expertise will guide clients as they navigate the landscape of ML algorithms, tools, and frameworks. Work with us to solve real-world challenges and define ML strategy for Air Force Agentic Systems. Join us. The world can't wait. You Have: - Experience deploying production grade ML models onto cloud environments - Experience in managing ML workloads to design elastic infrastructure to scale as needed to provide cost effective resource management for clients - Knowledge of MLOps Frameworks and Container Systems, such as Kubernetes or Docker - Bachelor's degree in Computer Science or Software Engineering 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 $77,500.00 to $176,000.00 (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. 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 As an MLOps / LLMOps Engineer, you will design, automate, and operate scalable ML and LLM systems on Irth’s enterprise Lakehouse platform. You will work closely with Data Science, Engineering, and Product teams to deploy reliable, secure, and production-ready ML and GenAI solutions. This role focuses on operationalizing ML models, building CI/CD pipelines, ensuring governance and compliance, and maintaining high-performance, observable AI systems. Qualifications - 3–5 years of experience in MLOps, LLMOps, or ML platform engineering roles. - Hands-on experience with Databricks, Delta Lake, Unity Catalog, and ML deployment workflows. - Strong experience with CI/CD pipelines using GitHub Actions and infrastructure automation. - Experience implementing data quality validation, schema governance, and data contracts. - Experience building production-grade ML pipelines with monitoring and observability. - Strong security knowledge including RBAC, encryption, data residency, and governance practices. - Proficiency in Python, SQL, and distributed data processing frameworks. Requirements - Deploy and manage ML and GenAI solutions including risk scoring, anomaly detection, predictive maintenance, NLP, and RAG pipelines. - Build and optimize LLM pipelines using vector databases, model serving endpoints, and inference workflows. - Optimize models using quantization, caching, and performance tuning techniques. - Implement batch and real-time inference pipelines with defined SLAs. - Implement data contracts, schema validation, and data quality checks across ML pipelines. - Ensure secure handling of sensitive data including PII detection, classification, and obfuscation. - Maintain full lineage from data sources to deployed models and serving endpoints. - Enforce data residency, governance, and compliance policies. - Implement CI/CD pipelines using GitHub Actions and Databricks Asset Bundles. - Automate deployments across DEV, QA, and PROD environments. - Develop unit and integration tests for data pipelines and ML models. - Ensure version control, reproducibility, and automated deployment workflows. - Monitor pipeline health, model performance, drift, and system reliability. - Implement alerting, incident response workflows, and automated ticketing. - Track LLM performance metrics including latency, hallucination rates, and API costs. - Develop runbooks, disaster recovery procedures, and operational documentation. - Apply tagging policies and cost tracking for ML infrastructure. - Support budget monitoring, cost optimization, and resource management. Benefits - Being an integral part of a dynamic, growing company that is well respected in its industry. - Competitive pay based on experience. Company Description Irth Solutions is a software product company building cutting-edge technology platforms that enable data-driven insights across Damage Prevention, Asset Integrity, Land Management, and Stakeholder Engagement. With a strong product culture, collaborative environment, and high growth potential, Irth offers opportunities to work on enterprise-scale data and AI platforms.

• utilize in-house and 3rd party LLMs to solve language understanding problems • employ techniques such as fine-tuning and RAG to improve models • contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development • help drive optimization, testing, and tooling to improve quality of our content enrichment assets • collaborate with cross-functional teams of MLEs, data and backend engineers, and other stakeholders including tech research, data science, and product to develop new features and technologies • be a participant in our AI Foundation’s ML community and work collaboratively and efficiently within our existing platforms and systems • perform data analysis to establish baselines and inform product decisions • stay up-to-date on the latest machine learning algorithms and techniques