Senior Machine Learning Engineer

BizFirst•Alexandria, VA

2d•Hybrid

About The Position

BizFirst is assisting our client with the hiring of a Senior Machine Learning Engineer to help design, build, and deploy production -grade machine learning systems that will fundamentally reshape how the organization operates internally. This is a high -impact role at the center of the client’s AI transformation effort, working across data pipelines, model development, and production deployment in a collaborative, fast -moving environment. Our client is a mid -market professional services organization that is actively rethinking how it designs and executes its core business operations through artificial intelligence and automation. The company is building a dedicated AI capability to embed machine learning and generative AI into its most critical internal workflows – from decision support and process automation to real -time analytics and intelligent document processing. The ideal candidate will have significant experience (7–10 years) in machine learning engineering, with a strong background in building and shipping models at scale in production environments. Experience working on large -scale data systems and collaborating closely with data scientists, product teams, and platform engineers is essential. Hands -on experience with large language models (LLMs) and generative AI frameworks is strongly preferred.

Requirements

US Citizen or Permanent Resident authorized to work in the United States.
Experience: 7–10 years of experience in machine learning engineering or applied ML, with a strong emphasis on production systems.
ML Frameworks: Expert -level proficiency in PyTorch, TensorFlow, or equivalent frameworks, with a proven record of shipping models to production.
Engineering: Advanced Python skills; comfort with distributed systems, containerization (Docker/Kubernetes), and cloud -based ML infrastructure (AWS, GCP, or Azure).
Data: Solid command of feature engineering, data versioning, and large -scale data processing (Spark, Ray, or similar).
Collaboration: Strong ability to work across technical and non -technical stakeholders, clearly communicating model behavior, tradeoffs, and limitations.

Nice To Haves

Hands -on experience with large language models (LLMs), fine -tuning, retrieval -augmented generation (RAG), or prompt engineering pipelines.
Familiarity with MLOps platforms such as MLflow, Weights & Biases, or Kubeflow.
Experience building AI -powered internal tools, copilots, or automation workflows.
Background in enterprise or professional services environments.
Advanced degree (MS or PhD) in Machine Learning, Computer Science, Statistics, or a related field.

Responsibilities

Design, develop, and deploy scalable machine learning models and pipelines into production environments.
Translate business problems into well -scoped ML solutions in close collaboration with data scientists, engineers, and business stakeholders.
Build and maintain end -to -end ML pipelines from data ingestion and feature engineering through model serving and monitoring.
Lead model evaluation, A/B testing, and ongoing performance monitoring across deployed systems.
Partner with MLOps and platform engineering teams to ensure reliable, reproducible, and cost -effective model deployment.
Drive technical decisions on ML frameworks, model architectures, and tooling standards across the AI practice.
Mentor and develop junior ML engineers, establishing team -wide engineering standards and code quality practices.
Document model design decisions, experiment results, and deployment configurations to support organizational learning.