{"id":3411178,"url":"https://alion.io/job/node-digital-intelligent-automationai-ml-engineer","title":"Intelligent Automation/AI-ML Engineer","company":{"id":5581,"name":"Node.Digital","domain":"node.digital","url":"https://alion.io/company/node-digital","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Herndon, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":137000,"max_usd":306000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"DVC","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"NLP","optional":false},{"name":"OCR","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Transformers","optional":false},{"name":"Agile","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"Grafana","optional":true},{"name":"Kubeflow","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"n8n","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Power Automate","optional":true},{"name":"Prefect","optional":true},{"name":"Prometheus","optional":true},{"name":"UiPath","optional":true}],"status":"live","first_seen_at":"2026-10-28T17:53:40Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-30T21:37:56Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Intelligent Automation/AI-ML Engineer\nLocation: Herndon, VA (Hybrid Work)\nPreferred: US Citizenship\nExperience: 3+ yrs & 5 yrs\nRole Summary\nThe Intelligent Automation / AI-ML Engineer is the architect and guardian of intelligent automation solutions that incorporate AI/ML technologies, built on open-source AI models and the Model Context Protocol (MCP). The role ensures the operational efficiency and continuous refinement of integrated AI/ML solutions. Responsibility spans the design, maintenance and optimization of intelligent applications, as well as troubleshooting and resolving issues that arise after implementation.\nThe engineer also explores new ways to embed artificial intelligence and machine learning into automations to support continuous improvement, learning and augmented decision making, and builds repeatable intelligent solutions and bots for document processing, data cleansing and similar use cases. The role owns token management and optimization, model usage, performance, retraining and MLOps.\nKey Responsibilities\nSolution Design and Development\nArchitect, design and build intelligent automation solutions that embed AI/ML capabilities using open-source models and MCP-based integrations.\nDevelop MCP servers and clients that securely connect models to enterprise tools, data sources and workflows.\nBuild repeatable, reusable intelligent solutions and bots for document processing (classification, extraction, summarization, validation) and data cleansing (deduplication, normalization, enrichment, anomaly detection).\nDesign agentic and human-in-the-loop workflows that support augmented decision making.\nCreate reusable components, templates and patterns that accelerate delivery across the organization.\nOperations, Maintenance and Support\nMaintain and optimize deployed AI/ML solutions for reliability, accuracy and operational efficiency.\nTroubleshoot and resolve post-implementation issues, including model drift, integration failures, data quality problems and performance degradation.\nMonitor production solutions, define SLAs/SLOs, and perform root-cause analysis and corrective action.\nMaintain documentation, runbooks and knowledge base articles for supported solutions.\nMLOps and Model Lifecycle Management\nImplement and manage MLOps pipelines covering versioning, CI/CD, automated testing, deployment, monitoring and rollback.\nOwn model retraining strategy, including drift detection, data and label management, evaluation benchmarks and scheduled or triggered retraining.\nTrack experiments, datasets and model artifacts using tools such as MLflow, DVC or equivalent.\nEvaluate, fine-tune and select open-source models, balancing accuracy, latency, cost and licensing.\nEstablish model governance, including auditability, reproducibility, bias and safety checks, and responsible AI practices.\nToken Management, Optimization and Performance\nImplement token management and usage controls, including budgets, quotas, rate limiting, caching and cost attribution by team or use case.\nOptimize prompts, context windows, retrieval strategies and model routing to reduce token consumption and cost without sacrificing quality.\nMonitor and tune model usage and performance: latency, throughput, GPU/CPU utilization, quantization, batching and inference serving.\nProduce dashboards and reports on usage, cost, quality and ROI.\nInnovation and Continuous Improvement\nExplore and prototype new options for embedding AI/ML into automations to enable continuous improvement and learning.\nEvaluate emerging open-source models, frameworks, MCP capabilities and automation platforms, and recommend adoption where valuable.\nPartner with business stakeholders to identify automation opportunities, define success metrics and measure outcomes.\nSecurity, Compliance and Collaboration\nApply security best practices for data privacy, access control, secrets management and prompt-injection and data-leakage mitigation.\nEnsure solutions comply with organizational policies and relevant regulations.\nCollaborate with data engineers, software engineers, security teams, business analysts and process owners.\nMentor team members and share best practices.\nRequirements\nRequired Qualifications\nBachelor's degree in Computer Science, Data Science, Engineering or a related field (or equivalent experience).\nExperience in AI/ML engineering, intelligent automation or software engineering, with production deployments.\nStrong Python skills and experience with ML/LLM frameworks such as PyTorch, Hugging Face Transformers, LangChain/LlamaIndex or similar.\nHands-on experience with open-source LLMs, including deployment and inference serving and fine-tuning techniques.\nPractical experience with the Model Context Protocol (MCP) or comparable tool-integration and agent frameworks.\nExperience building document processing solutions (OCR, NLP, information extraction) and data cleansing pipelines.\nWorking knowledge of MLOps practices and tools (CI/CD, containerization with Docker/Kubernetes, model registries, monitoring).\nExperience with token usage tracking, cost optimization and performance tuning of LLM applications.\nFamiliarity with RAG architectures, vector databases and embeddings.\nStrong troubleshooting, analytical and communication skills.\nPreferred Qualifications\nExperience with RPA or workflow platforms (e.g., UiPath, Automation Anywhere, Power Automate, n8n, Camunda).\nCloud experience (AWS, Azure or GCP), including GPU workloads and managed ML services.\nExperience with orchestration tools (Airflow, Prefect, Kubeflow) and observability tools (Prometheus, Grafana, OpenTelemetry, LLM tracing tools).\nKnowledge of model evaluation frameworks, guardrails and responsible AI.\nRelevant certifications in cloud, ML or automation platforms.\nExperience working in an Agile/DevOps environment.\nBenefits\nMedical 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