Senior AI Scientist and Enterprise ML Lead

Bechtel Corporation; Reston (VA); Hybrid; USD 123,000 - 179,000; Part time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

Disability insurance

Paid time off

Paid holidays

Parental leave

401K with matching

Job summary

Bechtel Corporation in Reston, VA, seeks a Senior Data Scientist to lead end-to-end AI/ML initiatives across EPC programs, embedding AI into live project environments and driving responsible, scalable solutions.

You will mentor data scientists, set technical direction, coordinate with stakeholders, and ensure deployment, MLOps, and governance meet enterprise standards while delivering measurable impact on schedule and cost.

Qualifications

• Bachelor's degree in a related field or international equivalent.
• 8–14 years of relevant AI/ML experience, with production-grade model delivery.

Responsibilities

• Lead the design and delivery of end-to-end AI/ML solutions for complex problems spanning programs and products.
• Set technical direction and guide solution architecture for scalable data and ML systems.
• Champion cutting-edge AI methods (generative AI, retrieval, RL) and apply them pragmatically.
• Own deployment, CI/CD, observability, and optimization with measurable SLAs and alerts.
• Design and operate systems that consolidate unstructured data at scale with governance and reproducibility.

Skills

NLP

Computer Vision

Deep Learning

Python

AI/ML Frameworks

Azure AI/ML

MLOps

Docker

Kubernetes

Data Engineering

Communication

Education

Bachelor's degree

International equivalent

Tools

TensorFlow

PyTorch

Keras

scikit-learn

Azure Data Factory

Databricks

Kafka

Docker

Kubernetes

SQL

NoSQL

LLMs

Azure AI

Job description

Bechtel Corporation in Reston, VA, seeks a Senior Data Scientist to lead end-to-end AI/ML initiatives across EPC programs, embedding AI into live project environments and driving responsible, scalable solutions.

You will mentor data scientists, set technical direction, coordinate with stakeholders, and ensure deployment, MLOps, and governance meet enterprise standards while delivering measurable impact on schedule and cost.