AI Solutions Engineer

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Job Summary:

The AI Program is a strategic cross-business initiative delivering AI capabilities at scale across Bechtel Infrastructure as part of the wider Execution Transformation Initiative.  The AI Program relies on rigorous technical assurance to ensure that AI models, agents, and coding solutions deployed across Bechtel meet quality, safety, and accuracy standards. As AI capabilities scale from pilots to production, structured validation frameworks, coding standards, and Human-in-the-Loop (HITL) review processes are essential to maintaining trust in AI-driven outputs.

The AI Solutions Engineer will serve as the technical quality authority within the AI programme, responsible for establishing and enforcing AI coding standards, validating machine learning models and AI agents, and maintaining robust testing frameworks that safeguard the integrity of AI solutions across the organisation. This role reports to the Data Solutions Architect and works in close collaboration with the AI Portfolio & Governance Manager and functional AI leads.

"This position is designated as part-time telework per our global telework policy and will require at least three days of in-person attendance per week at the assigned office or project. Weekly in-person schedules will be determined by the individual and their supervisor, in consultation with functional or project leadership” #LI-RM1 

Major Responsibilities:

• Establish and enforce AI coding standards and development best practices across the AI programme, ensuring consistent quality and maintainability of AI solutions.
• Validate machine learning models and AI agents against defined performance, accuracy, and safety criteria prior to deployment.
• Serve as the Human-in-the-Loop (HITL) reviewer, providing structured oversight and approval authority for AI-generated outputs. Conduct solution assurance reviews prior to the deployment or broader rollout of AI capabilities across business units.
• Design, implement, and maintain testing frameworks and evaluation pipelines for AI solutions including LLM-based applications and agentic workflows.
• Collaborate with Data Solutions Architect to ensure AI solutions align with platform architecture, data governance standards, and security requirements.
• Identify and mitigate risks related to model performance, hallucination, bias, and data quality, proposing and implementing corrective actions.
• Maintain documentation of model performance benchmarks, test results, and validation outcomes to support programme governance.
• Support the integration of AI agents and large language model (LLM)-based tools into project delivery workflows.
• Contribute to the development of technical AI governance policies, responsible AI frameworks, and operational guardrails for the AI Program.
• Research emerging AI and machine learning technologies, including developments in large language models, AI agents, evaluation techniques, and model deployment patterns, to ensure the AI Program adopts appropriate, current, and technically robust solutions.
• Provide technical training, guidance, and coaching to the AI Program Team to build internal capability, strengthen technical expertise, and promote consistent application of AI engineering, testing, and assurance practices.

Education and Experience Requirements:

• Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related field.
• 7+ years or more of experience in AI/ML engineering or data science roles.

Required Knowledge and Skills:

• Proficiency in Python and experience with machine learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow, or Hugging Face).
• Demonstrated experience in AI model validation, testing, and quality assurance, including evaluation of LLM or generative AI outputs.
• Experience designing AI evaluation frameworks, test cases, benchmarks, or quality gates for machine learning, generative AI, or LLM-based solutions. Preferably hands-on with the tooling provided in Databricks
• Understanding of large language models (LLMs), AI agents, prompt engineering, and agentic frameworks.
• Strong analytical and problem-solving skills with high attention to detail and a rigorous approach to quality.
• Ability to communicate technical findings and assurance outcomes clearly to both technical and non-technical stakeholders.
• Experience preparing clear technical documentation, validation reports, model performance summaries, or governance evidence for AI/ML solutions.

  Preferred Qualifications

• Experience with MLOps platforms and model deployment pipelines (e.g., Azure Machine Learning, MLflow, or equivalent).
• Familiarity with responsible AI frameworks, bias detection methodologies, and model governance practices.
• Experience with agentic AI platforms such as Microsoft Copilot Studio, AutoGen, LangChain, or similar frameworks.
• Hands-on experience with Azure cloud services and DevOps practices including CI/CD pipelines and version control (Git).
• Experience providing technical guidance, coaching, or training to engineering, data, or AI delivery teams.
• Knowledge of EPC industry workflows, engineering data standards, and project delivery environments.
• Experience with RAG (Retrieval-Augmented Generation) architectures and enterprise AI deployment patterns.
• Demonstrated commitment to staying current with emerging AI, machine learning, LLM, and agentic AI technologies and translating relevant developments into practical implementation guidance.

Total Rewards/Benefits:

For decades, Bechtel has worked to inspire the next generation of employees and beyond! Because our teams face some of the world's toughest challenges, we offer robust benefits to ensure our people thrive.  Whether it is advancing careers, delivering programs to enhance our culture, or providing time to recharge, Bechtel has the benefits to build a legacy of sustainable growth. Learn more at Bechtel Total Rewards

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