Interested in this AI/ML Engineer role at Echo Global Logistics?
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About This Role
The Data Science Operations Lead sits at the intersection of Data Science, Engineering, and IT Architecture: a senior individual\-contributor role focused on the operational side of the model lifecycle, including deployment, monitoring, scaling, and maintenance. Echo runs a growing portfolio of models in production, and this role exists to keep that portfolio reliable, observable, and well\-governed without pulling our Data Scientists away from building new capabilities. The Lead is the team's resource for moving models from R\&D to production services, the first line on production issues, and the standing point of contact with Architecture on everything deployment\- and reliability\-related.What You'll Own
- Model deployment partnership. Serve as Data Science's primary counterpart to the Architecture / Platform Engineering team on model deployment. Own the day\-to\-day collaboration, hand\-offs, and coordination. Data Scientists typically hand off a trained model and its training data. Engineering needs a running service: an API, a web tool, something the business can call. Your job is to bridge that gap.
- Production reliability and incident response. Act as first point of contact for production issues (outages, errors, degraded endpoints) across all deployed models and endpoints. This role carries an explicit on\-call / off\-hours availability expectation; production issues don't keep business hours, and shielding the development team from that interruption is central to the job.
- Resilient, error\-aware systems. Bring rigor to error handling and fault tolerance. Design and enforce practices that prevent errors before they happen and ensure models and endpoints degrade or fail gracefully, with sensible fallbacks, retries, alerting, and recovery paths.
- Monitoring and observability. Establish and maintain the monitoring and observability needed to manage a portfolio of production models as an enterprise capability by tracking model health, endpoint performance, latency, logging, and prediction quality.
- Deployment expertise and team enablement. Develop a detailed, working understanding of the deployment system as it continues to evolve, and act as the team's guide. Help Data Scientists move from experiment to production quickly and safely, and drive the templating, documentation, and automation that reduce the time the team spends on infrastructure.
- Governance and quality. Own versioning, reproducibility, and operational governance for models in production, partnering with Architecture on the standards and controls that keep our model and algorithm footprint trustworthy.
Who You Might Be
This role sits at the intersection of data science and software/DevOps, and strong candidates arrive from either side of that line:
- A software, DevOps, or platform engineer who has grown toward data science, having started in infrastructure, CI/CD, or production operations and since learned how data science models are built, served, and monitored.
- A data scientist who has grown toward infrastructure, DevOps, and MLOps, having started by building models and since moved deliberately toward deployment, reliability, and the engineering discipline of keeping models healthy in production.
What Success Looks Like
- The Data Science team spends materially less time on deployment logistics and incident response, and more on new development.
- Production issues are caught early, triaged quickly, and resolved or escalated cleanly, with clear ownership.
- Deployment becomes a repeatable, well\-understood path for the team rather than a per\-model project.
- Data Science and Architecture operate as two well\-aligned sides of one bridge.
Qualifications
Required
- Hands\-on experience operating ML or software systems in production: an MLOps, DevOps, SRE, platform, or data science background with demonstrated production ownership.
- Strong working knowledge of CI/CD pipelines, deployment automation, and a major cloud platform (AWS, Azure, or GCP).
- Demonstrated expertise in error handling, fault tolerance, and designing systems that fail gracefully (retries, fallbacks, alerting, monitoring/observability).
- Proficiency in Python (R a plus), and a working understanding of how ML models are packaged, served, monitored, and retrained.
- Comfort serving as first point of contact for production issues, including an on\-call / off\-hours expectation.
- A teaching disposition, with the ability to translate complex infrastructure into clear guidance for colleagues who are not infrastructure specialists.
Preferred
- Experience standing up monitoring and observability for a portfolio of production models or services (e.g., drift detection, performance tracking, alerting).
- Familiarity with containerization (Docker) and orchestration (Kubernetes), infrastructure\-as\-code, and model\-serving frameworks.
- Familiarity with MLOps tooling such as MLflow, Airflow, or Kubeflow, or managed equivalents (e.g., SageMaker, Vertex AI), and with data/model versioning.
- Experience working across an engineering/architecture boundary as a liaison or embedded operations partner.
- Pragmatic use of AI tooling to accelerate operations and code\-quality work, paired with sound judgment about when human reasoning is required.
Echo Global Logistics is a leading provider of technology\-enabled transportation management services. As a third\-party logistics provider, we simplify transportation management for our clients and carriers, handling crucial tasks so they can focus on what they do best. From coast to coast, dock to dock, and across all major transportation modes, Echo connects businesses that need to ship their products with carriers who transport goods quickly, securely, and cost\-effectively.
Work environment/physical demands summary:
This job operates in an office environment and uses a computer, telephone and otheroffice equipment as needed to perform duties. The noise level in the work environment is typical of that of an office with an open seating floor plan. The employee may encounter frequent interruptions throughout the work day. The employee is regularly required to sit, talk, or hear.
*All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, status as a qualified individual with a disability, or Vietnam era or other protected veteran.*
*\#LI\-SG1*
*\#Remote*
Benefits
For more information about our benefit offerings, please visit our careers page at https://www.echo.com/company/careers.
Compensation
$129,352\.00\-188,077\.00 per year
This role is eligible for a bonus that is based on a combination of personal and business performance.
Salary Context
This $129K-$188K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Echo Global Logistics, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($158K) sits 14% below the category median. Disclosed range: $129K to $188K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Echo Global Logistics AI Hiring
Echo Global Logistics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $188K - $188K.
Location Context
AI roles in Chicago pay a median of $200,100 across 329 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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