AI/ML Engineer

Since 1898, we have helped customers complete more than 25,000 projects in 160 countries on all seven continents that have created jobs, grown economies, improved the resiliency of the world's infrastructure, increased access to energy, resources, and vital services, and made the world a safer, cleaner place.

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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/ML Engineer will serve as the technical quality authority within the AI program. This role is responsible for establishing AI engineering, coding, and testing standards; designing and implementing validation and evaluation pipelines for machine learning models, generative AI applications, RAG solutions, and agentic workflows; and implementing technical guardrails that safeguard solution integrity.

"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

* Technical Quality Authority: Establish, maintain, and assess compliance with AI engineering standards, software design patterns, testing requirements, documentation expectations, and development best practices across the Infrastructure AI Program.
* Automated Evaluation Pipelines: Design, build, and maintain automated testing frameworks and evaluation pipelines using technologies such as MLflow and Ragas for traditional machine learning models, LLM-based applications, RAG solutions, and single-agent or multi-agent workflows.
* Model & Agent Assurance: Validate AI models and agentic systems against defined, measurable, and risk-based criteria for performance, accuracy, reliability, groundedness, safety, security, and responsible AI before production deployment or broader rollout.

* HITL Architecture: Architect and oversee risk-based Human-in-the-Loop validation mechanisms, including reviewer roles, review criteria, approval thresholds, sampling strategies, exception handling, evidence requirements, and escalation paths. Provide documented technical assurance recommendations for high-risk AI workflows to support formal governance and deployment decisions.

* Risk & Drift Mitigation: Identify model degradation, data drift, concept drift, bias, AI-specific security risks, and other quality issues. Define required corrective actions, collaborate with accountable solution owners on remediation, and verify that identified issues have been appropriately addressed.

* Platform & Governance Alignment: Collaborate with the Data Solutions Architect and platform teams to integrate automated tests and technical quality gates into approved MLOps platforms, Databricks workflows, Azure DevOps processes, and CI/CD pipelines. Ensure alignment with applicable architecture, data governance, cybersecurity, and responsible AI requirements.
* Benchmarking & Reporting: Maintain comprehensive technical documentation, model validation reports, and benchmark tracking to support program governance and auditability.
* Technical Guidance: Provide coaching, technical code reviews, and training to engineering and data delivery teams on AI testing methodologies, guardrails, and quality assurance.

* Education: Bachelor's or Master's degree in Data Science, Computer Science, Machine Learning, Statistics, or a related quantitative engineering field.
* Experience: 5+ years of progressive technical experience in Data Science, Machine Learning Engineering, or AI Quality/MLOps roles, with demonstrated hands-on experience deploying or evaluating production AI/ML systems.

* Core AI/ML Engineering & Coding: Advanced proficiency in Python and hands-on experience developing maintainable AI or machine learning solutions using relevant frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, Pandas, or NumPy.

* AI Evaluation & Quality Frameworks: Proven experience designing quantitative evaluation suites, test cases, and quality gates for LLMs, RAG architectures, and agentic solutions (preferably hands-on with tools like Databricks LLM Evaluation, MLflow, TruLens, or Ragas).
* Generative AI Concepts: Deep understanding of Transformer models, vector embeddings, prompt engineering, agent orchestration, and retrieval-augmented generation (RAG).
* MLOps & Pipeline Integration: Direct experience integrating automated model tests, evaluation pipelines, and assurance gates into CI/CD workflows and MLOps platforms such as Databricks or Azure Machine Learning, using Git-based version control and development practices.
* AI/ML Metrics Rigor: Strong background evaluating both statistical ML metrics (precision/recall, ROC-AUC, MAE) and generative AI metrics (faithfulness, answer relevance, semantic similarity, toxicity).
* Documentation & Communication: Ability to translate complex statistical and model validation metrics into technical governance reports and present findings clearly to leadership.
Preferred Qualifications:
* Experience with agentic frameworks (e.g., AutoGen, LangChain, CrewAI, or Microsoft Copilot Studio).
* Familiarity with responsible AI frameworks, guardrail libraries (e.g., NeMo Guardrails), and bias detection methodologies.
* Knowledge of EPC industry workflows, engineering data formats, or heavy asset project delivery environments.

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