Machine Learning Engineer

Company Overview

Throughout our worldwide network of experts, clients and communities, we are renowned for our leadership in fire protection engineering – a legacy of responsibility we have proudly upheld since 1939. Today, our expertise extends broadly across closely related security and risk-based fields – from accessibility consulting and risk analysis to process safety, forensic investigations, security risk consulting, emergency management, digital innovation and more.

Our engineers and consultants collaborate to solve complex safety and security challenges, ensuring our clients can protect what matters most. For over 80 years, we have helped mitigate risks that threaten lives, property and reputations. Through technology, expertise and industry-leading research, we remain dedicated to our purpose of making our world safe, secure and resilient.

At Jensen Hughes, we believe that creating and sustaining a culture of trust, integrity and professional growth starts with putting our people first. Our employees are our greatest strength, and we value the unique perspectives and talents they bring to our organization. 

Our wide range of Global Employee Networks connect people from across the organization, supporting career development and providing forums for individuals to share experiences on topics they're passionate about. Together, we are cultivating a connected culture where everyone has the opportunity to learn, grow and succeed together.

Job Overview

Jensen Hughes is seeking a Machine Learning (ML) Engineer with a strong programming foundation and passion for continuous learning to join our innovative team. This role offers exceptional opportunities to develop cutting-edge generative AI solutions while expanding your skills across diverse engineering disciplines including fire protection, forensics, security, risk assessment, accessibility consulting, and emergency management.

Jensen Hughes is a global leader in specialty engineering and consulting services for the built environment. As an ML Engineer, you'll have the unique opportunity to apply generative AI and machine learning across multiple engineering domains while continuously learning from world-class subject matter experts. This role emphasizes professional growth, technical skill development, and building strong communication abilities to bridge the gap between advanced generative AI techniques and practical engineering solutions.

Key Areas of Impact:

The successful candidate will contribute to innovative generative AI and ML projects while developing expertise across engineering disciplines:

• Generative AI Development: Design and implement large language models, document generation systems, and AI-powered automation tools for engineering workflows
• Intelligent Content Creation: Build systems that automatically generate technical reports, compliance documentation, and engineering deliverables
• Conversational AI Systems: Develop chatbots and AI assistants that help engineers and clients access technical expertise and engineering knowledge
• Multi-Modal AI Applications: Create solutions that process and generate text, images, and technical diagrams for engineering analysis
• Cross-Domain Learning: Gain expertise in fire protection engineering, forensic analysis, security consulting, and risk assessment through hands-on project work
• Technical Communication: Develop skills in translating complex generative AI concepts for engineering teams and presenting AI solutions to clients
• AI-Powered Engineering Tools: Build robust, scalable generative AI systems that integrate with existing engineering workflows
• Process Innovation: Create AI-powered tools that enhance engineering productivity and solution quality through intelligent automation

Responsibilities

As an ML Engineer, you will grow professionally while contributing to transformative generative AI and engineering solutions:

• Develop, fine-tune, and deploy generative AI models for various engineering applications
• Build AI-powered content generation systems for technical documentation and reports
• Create conversational AI interfaces that democratize access to engineering expertise
• Implement prompt engineering and retrieval-augmented generation (RAG) systems
• Collaborate with engineering consultants to understand requirements and translate them into AI solutions
• Build and maintain generative AI pipelines, APIs, and integration systems
• Participate in client meetings and technical presentations to develop communication skills
• Contribute to proposal development and business development activities with AI innovation focus
• Learn engineering fundamentals through mentorship and hands-on project experience
• Implement best practices for generative AI model versioning, testing, and deployment
• Document AI solutions and create knowledge-sharing materials for the team
• Support junior developers and contribute to team technical growth in generative AI

Requirements and Qualifications

Minimum Requirements:

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field
• Strong programming background with proficiency in Python and experience with ML frameworks and generative AI tools
• 3-5 years of professional software development or ML engineering experience
• Experience with generative AI models, large language models (LLMs), or AI content generation systems
• Excellent communication skills and enthusiasm for learning from technical experts
• Strong analytical and problem-solving abilities
• General engineering mindset with interest in understanding how systems work
• U.S. citizenship required to comply with 10 CFR Part 810 export control regulations

Preferred Qualifications:

• Engineering degree or coursework (any discipline - Mechanical, Civil, Electrical, Chemical, etc.)
• Experience with additional programming languages such as Python, JavaScript/React, or similar
• Strong familiarity with AWS Bedrock and Azure Copilot Studio for generative AI model deployment and development
• Experience with prompt engineering, fine-tuning LLMs, or building RAG systems
• Knowledge of OpenAI APIs, Anthropic Claude, or other generative AI platforms
• Experience with software engineering best practices (version control, testing, CI/CD)
• Background in consulting, client services, or collaborative technical environments
• Experience with API development and system integration
• Interest in or exposure to regulatory compliance, quality assurance, or technical documentation
• Demonstrated ability to learn new technical domains quickly
• Experience mentoring or teaching technical concepts to others

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