Intelligent Automation/AI-ML Engineer

Node.Digital•Herndon, VA

6h•Hybrid

About The Position

The Intelligent Automation / AI-ML Engineer designs, builds, maintains, and optimizes intelligent automation solutions incorporating AI/ML technologies, open-source models, and Model Context Protocol (MCP) integrations. The role supports a U.S. Federal Government project and requires U.S. citizenship to meet project eligibility and security clearance requirements. The position is based in Herndon, Virginia, with hybrid work. The posting lists experience as “3+ yrs & 5 yrs.” The engineer develops reusable intelligent solutions and bots for document processing and data cleansing, creates agentic and human-in-the-loop workflows, and supports augmented decision making. Responsibilities span production operations and troubleshooting, MLOps and model lifecycle management, token use and cost optimization, performance monitoring, security and compliance, and continuous improvement. The engineer partners with business stakeholders and technical teams to identify automation opportunities, measure outcomes, and share best practices.

Requirements

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; the posting states “3+ yrs & 5 yrs.”
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, 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 using OCR, NLP, and information extraction, and building data cleansing pipelines.
Working knowledge of MLOps practices and tools, including CI/CD, Docker/Kubernetes containerization, model registries, and 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.
U.S. citizenship is required for project eligibility and security clearance requirements.

Nice To Haves

Experience with RPA 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 ML services.
Experience with orchestration tools such as Airflow, Prefect, or Kubeflow, and observability tools such as Prometheus, Grafana, OpenTelemetry, or 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.

Responsibilities

Architect, design, and build intelligent automation solutions using open-source AI/ML models and MCP-based integrations.
Develop MCP servers and clients to securely connect models with enterprise tools, data sources, and workflows.
Build reusable solutions and bots for document classification, extraction, summarization, validation, and data cleansing tasks such as 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 to accelerate delivery.
Maintain and optimize deployed AI/ML solutions for reliability, accuracy, and operational efficiency.
Troubleshoot post-implementation issues, including model drift, integration failures, data quality problems, and performance degradation.
Monitor production solutions, define SLAs/SLOs, conduct root-cause analysis, and implement corrective actions.
Maintain documentation, runbooks, and knowledge base articles.
Implement and manage MLOps pipelines for 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 MLflow, DVC, or equivalent tools.
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.
Optimize prompts, context windows, retrieval strategies, and model routing to reduce token consumption and cost while maintaining 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 ROI.
Explore and prototype ways to embed AI/ML into automations for 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.
Apply security best practices for data privacy, access control, secrets management, and mitigation of prompt injection and data leakage.
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.