{"id":3812877,"url":"https://alion.io/job/sosi-machine-learning-engineer-mid","title":"Machine Learning Engineer - Mid","company":{"id":5998,"name":"SOSi","domain":"sosi.com","url":"https://alion.io/company/sosi","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"C","score":56,"open_postings":219,"ghost_share":0.74,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":31,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Huntsville, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":79679,"max":165488,"currency":"USD","period":"year","gross":null,"usd_annual":165488},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"GitLab","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Airflow","optional":true},{"name":"Anomaly Detection","optional":true},{"name":"Computer Vision","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GitLab CI","optional":true},{"name":"NLP","optional":true},{"name":"Triton Inference Server","optional":true}],"status":"live","first_seen_at":"2026-11-05T13:17:15Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T09:33:10Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":1},"description":"Founded in 1989 and headquartered in Reston, Virginia, SOSi is a private defense and government solutions business specializing in advanced technology systems and mission support. Its capabilities include data science, software development, network engineering, intelligence, surveillance, and reconnaissance (ISR), and logistics.\n SOSi is seeking a Mid-Level Machine Learning Engineer to support the development, integration, and deployment of machine learning capabilities for a government customer in Huntsville, Alabama.\nThe selected candidate will contribute to an MLOps environment supporting national security missions and large-scale analytical workloads. The position focuses on transforming machine learning prototypes into reliable, maintainable, and production-ready capabilities.\nThe ideal candidate has practical experience developing machine learning models, writing production-quality Python, and integrating models into established software systems. Candidates should understand the full machine learning lifecycle, including data preparation, experimentation, evaluation, deployment, monitoring, and continuous improvement.\nThe engineer will work alongside data scientists, data engineers, and software developers in an Agile environment to deliver solutions aligned with mission requirements.\nThe team works with technologies including Python, PyTorch, scikit-learn, MLflow, PostgreSQL, Docker, Linux, GitLab, Jupyter Notebooks, and supporting MLOps infrastructure.\nEssential Job Duties\nMachine Learning Development\nDevelop, train, evaluate, and improve machine learning models for operational applications.\nApply supervised and unsupervised learning techniques to structured and unstructured datasets.\nPerform feature engineering, data preprocessing, and dataset validation.\nSelect appropriate algorithms and evaluation metrics based on project requirements.\nConduct experiments, compare model performance, and document technical findings.\nIdentify and address model performance issues, including overfitting, data leakage, and prediction errors.\nProduction ML Engineering\nConvert research prototypes and notebooks into maintainable Python modules and services.\nIntegrate trained models into existing applications, APIs, and processing workflows.\nPackage and deploy model inference services using containerized environments.\nImplement consistent model loading, configuration management, and error handling.\nSupport model versioning, experiment tracking, and reproducible deployments.\nInvestigate and resolve model inference, integration, and performance issues.\nContribute to automated model validation and deployment workflows.\nSoftware Development and Quality Assurance\nWrite clean, modular, testable, and maintainable Python code.\nDevelop unit tests for preprocessing, feature engineering, and model-related functionality.\nDevelop integration tests validating model interactions with dependent systems.\nImplement smoke tests confirming deployed services operate as expected.\nParticipate in peer code reviews and address identified defects.\nUse Git-based development workflows and established CI/CD practices.\nMaintain technical documentation covering implementation, testing, and operational procedures.\nData Integration and MLOps\nCollaborate with data engineers to establish reliable training and inference datasets.\nImplement validation checks for dataset quality, schema consistency, and missing values.\nWork with PostgreSQL and other approved data sources.\nUse MLflow or comparable tools to manage experiments and model artifacts.\nSupport model registry workflows, version promotion, and rollback procedures.\nMonitor model behavior, resource utilization, and inference performance.\nIdentify data drift, performance degradation, and operational anomalies.\nSupport reproducible model training and deployment processes.\nCollaboration and Delivery\nComplete assigned development tasks within agreed sprint objectives.\nCommunicate implementation progress, technical blockers, and delivery risks.\nProvide realistic estimates for assigned engineering efforts.\nCoordinate interface requirements with data engineering and software development teams.\nFollow established architecture, coding, testing, and documentation standards.\nIdentify proposed scope changes before implementing unapproved functionality.\nParticipate in technical discussions, planning sessions, and code reviews.\n Minimum Requirements\nActive Top Secret clearance with SCI eligibility.\nBachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related quantitative discipline.\n3-5 years of relevant experience in machine learning engineering, software development, or applied data science.\nProficiency in Python and common machine learning libraries.\nPractical experience training, evaluating, and deploying machine learning models.\nExperience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.\nExperience using SQL and relational databases.\nWorking knowledge of Linux development environments and command-line tools.\nExperience with Git, collaborative development, and pull request workflows.\nFamiliarity with Docker and containerized application deployment.\nUnderstanding of automated testing, including unit and integration testing.\nKnowledge of REST APIs and integration between software components.\nUnderstanding of CI/CD principles and Agile development methodologies.\nAbility to independently implement, troubleshoot, and deliver assigned engineering tasks.\nPreferred Qualifications\nExperience deploying and maintaining machine learning models in production.\nExperience with MLflow or similar experiment tracking and model registry platforms.\nFamiliarity with Triton Inference Server or comparable inference serving platforms.\nExperience developing computer vision, NLP, classification, or anomaly detection solutions.\nExperience integrating machine learning models into ETL/ELT pipelines.\nFamiliarity with Apache Airflow or similar workflow orchestration tools.\nExperience with PostgreSQL, large-scale datasets, or distributed processing.\nUnderstanding of model monitoring, data drift, reproducibility, and rollback strategies.\nFamiliarity with GitLab CI/CD, automated validation, and container registries.\nExperience developing solutions in restricted, offline, or on-premises computing environments.\nExperience supporting DoD or Intelligence Community missions.\nExposure to high-performance computing and multi-INT analytical workloads.\n Work Environment\nOffice-based position in Huntsville, Alabama.\nCollaboration with multidisciplinary engineering and government stakeholder teams.\nWork involving mission-sensitive data and secure computing environments.\nOccasional remote work or travel may be available based on program requirements.\nWorking at SOSi\nAll interested individuals will receive consideration and 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