Node.Digital

Develops AI-driven digital solutions for missions

Intelligent Automation/AI-ML Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeHerndon, VA, USANo salary listed

About the Job

Requirements

A bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent experience.
Experience in AI/ML engineering, intelligent automation, or software engineering, including production deployments.
Strong Python skills and experience with machine-learning or large-language-model frameworks such as PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, or similar.
Hands-on experience with open-source large language models, including deployment, inference serving, and fine-tuning techniques.
Practical experience with the Model Context Protocol or comparable tool-integration and agent frameworks.
Experience building document-processing solutions using optical character recognition, natural language processing, and information extraction, as well as data-cleansing pipelines.
Working knowledge of MLOps practices and tools, including continuous integration and delivery, containerization with Docker or Kubernetes, model registries, and monitoring.
Experience with token-usage tracking, cost optimization, and performance tuning of large-language-model applications.
Familiarity with retrieval-augmented generation architectures, vector databases, and embeddings.
Strong troubleshooting and analytical skills.
U.S. citizenship is required to meet the project’s eligibility and security-clearance requirements.

Responsibilities

Architect, design, and build intelligent automation solutions that embed AI/ML capabilities using open-source models and Model Context Protocol-based integrations.
Develop Model Context Protocol servers and clients that securely connect models to enterprise tools, data sources, and workflows.
Build repeatable, reusable intelligent solutions and bots for document processing, including classification, extraction, summarization, and validation, and for data cleansing, including deduplication, normalization, enrichment, and anomaly detection.
Design agentic and human-in-the-loop workflows that support augmented decision-making.
Create reusable components, templates, and patterns that accelerate delivery across the organization.
Maintain and optimize deployed AI/ML solutions for reliability, accuracy, and operational efficiency.
Troubleshoot and resolve post-implementation issues, including model drift, integration failures, data-quality problems, and performance degradation.
Monitor production solutions, define service-level agreements and objectives, and perform root-cause analysis and corrective action.
Maintain documentation, runbooks, and knowledge-base articles for supported solutions.
Implement and manage MLOps pipelines covering versioning, continuous integration and delivery, automated testing, deployment, monitoring, and rollback.
Own model-retraining strategy, including drift detection, data and label management, evaluation benchmarks, and scheduled or triggered retraining.
Track experiments, datasets, and model artifacts using tools such as MLflow, DVC, or equivalent.
Evaluate, fine-tune, and select open-source models, balancing accuracy, latency, cost, and licensing.
Establish model governance, including auditability, reproducibility, bias and safety checks, and responsible AI practices.
Implement token-management and usage controls, including budgets, quotas, rate limiting, caching, and cost attribution by team or use case.
Optimize prompts, context windows, retrieval strategies, and model routing to reduce token consumption and cost without sacrificing quality.
Monitor and tune model usage and performance, including latency, throughput, GPU/CPU utilization, quantization, batching, and inference serving.
Produce dashboards and reports on usage, cost, quality, and return on investment.
Explore and prototype new options for embedding AI/ML into automations to enable continuous improvement and learning.
Evaluate emerging open-source models, frameworks, Model Context Protocol capabilities, and automation platforms, and recommend adoption where valuable.
Partner with business stakeholders to identify automation opportunities, define success metrics, and measure outcomes.
Apply security best practices for data privacy, access control, secrets management, and prompt-injection and data-leakage mitigation.
Ensure solutions comply with organizational policies and relevant regulations.
Collaborate with data engineers, software engineers, security teams, business analysts, and process owners.
Mentor team members and share best practices.

Desired Qualifications

Experience with robotic process automation or workflow platforms such as UiPath, Automation Anywhere, Power Automate, n8n, or Camunda.
Cloud experience with AWS, Azure, or GCP, including GPU workloads and managed machine-learning services.
Experience with orchestration tools such as Airflow, Prefect, or Kubeflow and observability tools such as Prometheus, Grafana, OpenTelemetry, or large-language-model tracing tools.
Knowledge of model-evaluation frameworks, guardrails, and responsible AI.
Relevant certifications in cloud, machine learning, or automation platforms.
Experience working in an Agile/DevOps environment.

Intelligent Automation/AI-ML Engineer

Node.Digital · Herndon, United States (Hybrid) · Full-time · Posted 2026-10-28

Workplace: hybrid

Department: EPIC - DHS

Description

Intelligent Automation/AI-ML Engineer

Location: Herndon, VA (Hybrid Work)

This position supports a U.S. Federal Government project. U.S. citizenship is required to meet the project’s eligibility and security clearance requirements.

Experience: 3+ yrs & 5 yrs

Role Summary

The 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.

The 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.

Key Responsibilities

Solution Design and Development

Architect, design and build intelligent automation solutions that embed AI/ML capabilities using open-source models and MCP-based integrations.
Develop MCP servers and clients that securely connect models to enterprise tools, data sources and workflows.
Build repeatable, reusable intelligent solutions and bots for document processing (classification, extraction, summarization, validation) and data cleansing (deduplication, normalization, enrichment, anomaly detection).
Design agentic and human-in-the-loop workflows that support augmented decision making.
Create reusable components, templates and patterns that accelerate delivery across the organization.

Operations, Maintenance and Support

Maintain and optimize deployed AI/ML solutions for reliability, accuracy and operational efficiency.
Troubleshoot and resolve post-implementation issues, including model drift, integration failures, data quality problems and performance degradation.
Monitor production solutions, define SLAs/SLOs, and perform root-cause analysis and corrective action.
Maintain documentation, runbooks and knowledge base articles for supported solutions.

MLOps and Model Lifecycle Management

Implement and manage MLOps pipelines covering versioning, CI/CD, automated testing, deployment, monitoring and rollback.
Own model retraining strategy, including drift detection, data and label management, evaluation benchmarks and scheduled or triggered retraining.
Track experiments, datasets and model artifacts using tools such as MLflow, DVC or equivalent.
Evaluate, fine-tune and select open-source models, balancing accuracy, latency, cost and licensing.
Establish model governance, including auditability, reproducibility, bias and safety checks, and responsible AI practices.

Token Management, Optimization and Performance

Implement token management and usage controls, including budgets, quotas, rate limiting, caching and cost attribution by team or use case.
Optimize prompts, context windows, retrieval strategies and model routing to reduce token consumption and cost without sacrificing quality.
Monitor and tune model usage and performance: latency, throughput, GPU/CPU utilization, quantization, batching and inference serving.
Produce dashboards and reports on usage, cost, quality and ROI.

Innovation and Continuous Improvement

Explore and prototype new options for embedding AI/ML into automations to enable continuous improvement and learning.
Evaluate emerging open-source models, frameworks, MCP capabilities and automation platforms, and recommend adoption where valuable.
Partner with business stakeholders to identify automation opportunities, define success metrics and measure outcomes.

Security, Compliance and Collaboration

Apply security best practices for data privacy, access control, secrets management and prompt-injection and data-leakage mitigation.
Ensure solutions comply with organizational policies and relevant regulations.
Collaborate with data engineers, software engineers, security teams, business analysts and process owners.
Mentor team members and share best practices.

Requirements

Required Qualifications

Bachelor's degree in Computer Science, Data Science, Engineering or a related field (or equivalent experience).
Experience in AI/ML engineering, intelligent automation or software engineering, with production deployments.
Strong Python skills and experience with ML/LLM frameworks such as PyTorch, Hugging Face Transformers, LangChain/LlamaIndex or similar.
Hands-on experience with open-source LLMs, including deployment and inference serving and fine-tuning techniques.
Practical experience with the Model Context Protocol (MCP) or comparable tool-integration and agent frameworks.
Experience building document processing solutions (OCR, NLP, information extraction) and data cleansing pipelines.
Working knowledge of MLOps practices and tools (CI/CD, containerization with Docker/Kubernetes, model registries, monitoring).
Experience with token usage tracking, cost optimization and performance tuning of LLM applications.
Familiarity with RAG architectures, vector databases and embeddings.
Strong troubleshooting, analytical and communication skills.

Preferred Qualifications

Experience with RPA or workflow platforms (e.g., UiPath, Automation Anywhere, Power Automate, n8n, Camunda).
Cloud experience (AWS, Azure or GCP), including GPU workloads and managed ML services.
Experience with orchestration tools (Airflow, Prefect, Kubeflow) and observability tools (Prometheus, Grafana, OpenTelemetry, LLM tracing tools).
Knowledge of model evaluation frameworks, guardrails and responsible AI.
Relevant certifications in cloud, ML or automation platforms.
Experience working in an Agile/DevOps environment.

Benefits

Medical
Dental
Vision
Basic Life
Health Saving Account
401K Matching
Three weeks of PTO/Sick
11 Paid Holidays
Pre-Approved Online Training

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About the Company

Node.Digital

Develops AI-driven digital solutions for missions

Node.Digital builds digital solutions using AI automation and machine learning to help clients achieve their missions. Its approach centers on designing and deploying AI-powered software that automates processes, analyzes data, and supports decision-making for complex, mission-focused problems. Unlike general software firms, Node.Digital emphasizes delivering tailored, mission-driven solutions that address each customer’s unique objectives. The company’s goal is to enable organizations to operate more effectively and achieve impactful outcomes by applying AI and ML to real-world challenges.

Company size: 11-50
Company stage: Not listed
Total funding: Not listed
Headquarters: Herndon, VA
Founded: 2017

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