Intelligent Automation/AI-ML Engineer

Node.Digital•Herndon, VA

1d•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 operational efficiency and continuous improvement by troubleshooting deployed solutions and exploring ways to embed AI/ML into automations for learning and augmented decision-making. The engineer develops reusable solutions and bots for document processing and data cleansing, and is responsible for token management and optimization, model usage and performance, model retraining, and MLOps. The role also partners with stakeholders and technical teams to identify opportunities, improve deployed systems, and ensure secure, compliant, and responsible use of AI.

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.
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 MCP or comparable tool-integration and agent frameworks.
Experience building document processing solutions involving OCR, NLP, and information extraction, and 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.

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.
US citizenship is preferred.

Responsibilities

Architect, design, and build intelligent automation solutions embedding AI/ML capabilities using open-source 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, including deduplication, normalization, enrichment, and anomaly detection.
Design agentic and human-in-the-loop workflows for 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 solution documentation, runbooks, and knowledge base articles.
Implement and manage MLOps pipelines for versioning, CI/CD, automated testing, deployment, monitoring, and rollback.
Manage model retraining strategies, 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 while balancing accuracy, latency, cost, and licensing.
Establish model governance practices covering auditability, reproducibility, bias and safety checks, and responsible AI.
Implement token 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 valuable adoption opportunities.
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.