Senior AI Engineer – Hybrid

Jobtailor; Maryland; On-site; USD 170,000 - 250,000; Full time

Job summary

Jobtailor seeks an senior AI/ML architect to lead technical direction for AI/ML across sales, engineering, and manufacturing systems. You will define model selection, data architectures, deployment topologies, and build-versus-buy decisions, partnering with cross-functional teams to prioritize AI work.

The role requires ownership of AI/ML systems in production, deep learning experience, and strong Python/SQL skills.

Qualifications

• Bachelor's degree required in a technical field.
• 5+ years in software or data engineering.
• 5+ years building ML/AI systems in production.
• Ownership of an AI/ML system in production.
• Strong Python and SQL experience.

Responsibilities

• Set technical direction for AI/ML architecture and strategy across sales, engineering, and manufacturing systems.
• Define model selection, data architecture, evaluation approach, deployment topology, and build-versus-buy recommendations.
• Partner with product management, sales operations, engineering leadership, and plant operations to prioritize AI work.
• Establish standards for reproducible training and evaluation, model and prompt versioning, monitoring, drift detection, rollback paths, and audit trails.
• Serve as senior technical voice in design reviews and mentor software and data engineers.
• Extract structured requirements from customer specs, PDFs, drawings, and spreadsheets.
• Support configuration recommendation, pricing and discount guidance, and cost estimation.
• Build similar-order retrieval, BOM anomaly detection, configuration rule mining, conflict detection, and engineering change impact analysis.
• Develop manufacturing intelligence for labor and routing prediction, material shortage and schedule risk forecasting, test-data anomaly detection, and quality defect prediction.
• Develop computer vision solutions for assembly verification, nameplate and label validation, or station completeness checks where appropriate.
• Build retrieval-grounded assistants and agents that use configurator, PLM, and ERP APIs.
• Design safe deployment with human-in-the-loop review, confidence thresholds, abstention, and traceability.
• Define ground truth and continuously measure production performance.
• Address on-premise and edge inference, latency, intermittent connectivity, OT/IT network segmentation, and plant cybersecurity constraints.
• Establish AI data governance for customer, pricing, and design data.
• Assess and improve data usability across CPQ, PLM, ERP, and MES.
• Build pipelines, feature engineering, and datasets for repeatable AI development.
• Partner with the Program Manager, owning technical direction and feasibility while the Program Manager owns scope, schedule, and stakeholder accountability.

Skills

Python Programming

SQL Proficiency

Machine Learning

Deep Learning Frameworks

LLM Applications

Education

Bachelor's degree in Computer Science, Engineering, Applied Mathematics, Statistics, or related field

Tools

CI/CD Tools

Cloud ML Platforms

Containerization

Job description

• Set technical direction for AI/ML architecture and technical strategy across sales, engineering, and manufacturing systems
• Define model selection, data architecture, evaluation approach, deployment topology, and build-versus-buy recommendations
• Partner with product management, sales operations, engineering leadership, and plant operations to prioritize AI work
• Establish standards for reproducible training and evaluation, model and prompt versioning, monitoring, drift detection, rollback paths, and audit trails
• Serve as senior technical voice in design reviews and mentor software and data engineers
• Extract structured requirements from customer specifications, mechanical schedules, submittal documents, PDFs, drawings, and spreadsheets
• Support configuration recommendation, competitive crossover matching, pricing and discount guidance, and cost estimation
• Build similar-order retrieval, BOM anomaly detection, configuration rule mining, conflict detection, and engineering change impact analysis
• Develop manufacturing intelligence for labor and routing prediction, material shortage and schedule risk forecasting, test-data anomaly detection, and quality defect prediction
• Develop computer vision solutions for assembly verification, nameplate and label validation, or station completeness checks where appropriate
• Build retrieval-grounded assistants and agents that use configurator, PLM, and ERP APIs
• Design safe deployment with human-in-the-loop review, confidence thresholds, abstention, and traceability
• Define ground truth and continuously measure production performance
• Address on-premise and edge inference, latency, intermittent connectivity, OT/IT network segmentation, and plant cybersecurity constraints
• Establish AI data governance for customer, pricing, and design data
• Assess and improve data usability across CPQ, PLM, ERP, and MES
• Build pipelines, feature engineering, and datasets for repeatable AI development
• Partner with the Program Manager, owning technical direction and feasibility while the Program Manager owns scope, schedule, and stakeholder accountability

Requirements

• Bachelor's degree in Computer Science, Engineering, Applied Mathematics, Statistics, or a related field
• 5+ years in software or data engineering
• 5+ years building machine learning or AI systems that reached production and real users
• Demonstrated ownership of an AI/ML system in production over time, not proofs of concept
• Strong Python and SQL
• Experience with classical and tabular ML, including gradient boosting and scikit-learn
• Experience with deep learning frameworks
• Experience with LLM application patterns including retrieval, structured extraction, tool use, and evaluation
• Fluency with ERP and PLM schemas, MES and historian data, and semi-structured documents
• Experience deploying and operating models in production, including containerization, CI/CD, cloud ML platforms, monitoring, and cost management
• Candidates should be located within commuting distance of the facility or be open to relocating
• Preferred: experience in manufacturing, industrial, or engineer-to-order environments
• Preferred: familiarity with BOM structures, variant and option modeling, routings and standard times, and engineering change processes
• Preferred: document AI and information extraction experience with engineering drawings, specifications, or technical documentation
• Preferred: optimization and operations research exposure
• Preferred: computer vision experience in an industrial setting
• Preferred: edge or on-premise inference experience in an OT environment
• Preferred: experience as one of the first senior AI hires, building the function rather than inheriting it

Core Competencies

Demonstrates expertise in AI/ML architecture and technical strategy, with a strong focus on model selection, data architecture, and deployment in production environments. Proficient in Python, SQL, and deep learning frameworks, with experience in manufacturing and industrial applications.

Highest-signal resume keywords

• AI/ML System Ownership
• Python Programming
• SQL Proficiency
• Deep Learning Frameworks
• Manufacturing Intelligence Development

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Data Architecture
• Model Evaluation
• Containerization
• CI/CD
• Cloud ML Platforms
• Feature Engineering
• Computer Vision
• Gradient Boosting
• Scikit-Learn

Soft Skills

• Mentoring
• Collaboration
• Technical Leadership

Industry Keywords

• Manufacturing
• Engineering Change Processes
• BOM Structures
• Variant Modeling
• Document AI

Tools & Technologies

• ERP
• PLM
• MES
• APIs
• CI/CD Tools
• Cloud Platforms