MLOps Engineer

BizFirst

Alexandria (VA)

On-site

USD 140,000 - 200,000

Full time

14 days+

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Benefits offered by this job

Family health care

Family dental

Family vision

Performance bonuses

Lifetime event bonuses

Profit-sharing

Unlimited leave

401k match

Training budget

Job summary

BizFirst is seeking an MLOps Engineer to build and operate the infrastructure, tooling, and processes that keep ML models running reliably in production. This foundational role sits at the intersection of data engineering, platform engineering, and applied ML—enabling data scientists and ML engineers to move faster and ship with confidence.

You will own CI/CD for ML, manage dockerized workloads, collaborate with data scientists, and implement monitoring and incident response for production

Qualifications

4–8 years in MLOps/DevOps or platform engineering with ML workload responsibility.
Proficiency with Docker, Kubernetes, and cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
Hands-on with orchestration tools such as Airflow, Prefect, Kubeflow Pipelines.
Experience with ML tooling such as MLflow or Weights & Biases for tracking and registry.
Strong Python skills and infrastructure-as-code (Terraform, Pulumi, or CloudFormation).

Responsibilities

Design, build, and maintain end-to-end ML pipelines from data ingestion to deployment.
Implement and manage CI/CD workflows for ML models from experimentation to production.
Own the model registry, versioning, and experiment tracking infrastructure.
Build monitoring and alerting for drift, data quality, and performance.
Manage containerized ML workloads with Docker and Kubernetes, including resource allocation.
Collaborate with data scientists to align infra needs and reduce friction.
Evaluate and adopt MLOps tools to mature operational practices.
Develop runbooks, docs, and incident response for production ML systems.

Skills

MLOps

DevOps

Platform engineering

Data engineering

Education

Bachelor's degree in Computer Science or Software Engineering

Tools

Docker

Kubernetes

Airflow

Prefect

Kubeflow Pipelines

MLflow

Weights & Biases

Terraform

Pulumi

CloudFormation

Job description

BizFirstis assisting our client with the hiring of an MLOps Engineer to build andoperate the infrastructure, tooling, and processes that keep machine learningmodels running reliably in production. This is a foundational role in theclient’s growing AI practice, sitting at the intersection of data engineering,platform engineering, and applied ML – where your work directly enables datascientists and ML engineers to move faster and ship with confidence.

Ourclient is a mid‑market professional services organization that is activelyrethinking how it designs and executes its core business operations throughartificial intelligence and automation. The company is building a dedicated AIcapability to embed machine learning and generative AI into its most criticalinternal workflows, from decision support and process automation to real-timeanalytics and intelligent document processing.

What will you do

Theideal candidate has 4–8 years of experience in MLOps, DevOps, or platform/dataengineering, with direct experience standing up and maintaining MLinfrastructure in cloud environments. You have worked with CI/CD pipelines,containerized ML workloads, and model registries – and you understand what ittakes to move models from a notebook to a production system that is observable,scalable, and maintainable.

Responsibilities

Design,build, and maintain end-to-end ML pipelines including data ingestion, featureengineering, model training, evaluation, and deployment.
Implementand manage CI/CD workflows for ML models, ensuring consistent, automated pathsfrom experimentation to production.
Ownthe model registry, versioning strategy, and experiment tracking infrastructureused across the AI team.
Buildmonitoring and alerting systems to detect model drift, data quality issues, andperformance degradation in deployed systems.
Managecontainerized ML workloads using Docker and Kubernetes, including scheduling,resource allocation, and cost optimization.
Collaborateclosely with data scientists and ML engineers to understand infrastructureneeds and reduce friction in the development lifecycle.
Evaluateand adopt MLOps tooling (orchestration, feature stores, serving frameworks) tomature the team’s operational practices.
Developrunbooks, documentation, and incident response procedures for production MLsystems.

Requirements

USCitizen or Permanent Resident authorized to work in the United States.

Experience:4–8 years in MLOps, platform engineering, or a DevOps role with direct MLworkload responsibility.

Infrastructure:Proficiency with Docker, Kubernetes, and cloud platforms (AWS SageMaker, GCPVertex AI, or Azure ML).

Pipelines:Hands-on experience with orchestration tools such as Airflow, Prefect, KubeflowPipelines, or similar.

MLTooling: Working knowledge of MLflow, Weights & Biases, or equivalentexperiment tracking and model registry platforms.

Programming:Strong Python skills; comfort writing infrastructure-as-code (Terraform,Pulumi, or CloudFormation).

Monitoring:Experience building observability into production ML systems – metrics,logging, alerting, and dashboards.

Preferred

Familiaritywith feature stores (Feast, Tecton, or similar) and online/offline featureserving patterns.

Backgroundworking in a fast-moving team where data scientists and ML engineers areprimary customers.

Experiencewith cost optimization strategies for large-scale cloud-based ML training andinference.

Degreein Computer Science, Software Engineering, or a related technical field.

JobType: Full-time, Permanent Position

Work Authorization

USCitizen or Permanent Resident; no active security clearance required.

Schedule

Mondayto Friday

Work Location

BizFirst LLC is an Equal Opportunity Employer and does not discriminate on the basis of race or ethnicity, religion, sex, national origin, age, veteran disability or genetic information or any other reason prohibited by law in employment.

Benefits