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About This Role
Location: Remote (U.S.\-based candidates).
Employment Type: 12\-18\-month contract. Possibility of renewal depending on performance and business needs.
Compensation: $45 \- $55/hr W2 hourly or $55 \- $65/hr C2C.
Requirements: must be authorized to work legally in the US without sponsorship, now or in the future.
About Us
Concord isn't your typical consulting firm; we are an execution company with a passion for making things happen. Our mission is to help clients enhance customer experiences, optimize operations, and revolutionize their product offerings through seamless integration, optimization, and activation of technology and data.
We are purpose\-built, merging the industry’s top specialty companies to amplify our Innovation Capabilities in analytics \& AI, data management \& engineering, UX and digital experience, and technical platform integration, automation \& security engineering.
About the Role
We are seeking a hands\-on AI Implementation Project Manager to lead delivery across a portfolio of strategic AI\-enabled solutions within a Global Strategic Sourcing and Procurement function. This is an execution\-focused role for a candidate who can drive programs from planning through deployment and adoption in a complex, global, matrixed environment.
The selected individual will coordinate across business, data, and technology teams as well as external partners and software vendors, working closely with an AI Enablement Team and broader cross\-functional stakeholders.
Key Responsibilities
- Lead end\-to\-end delivery of a portfolio of AI\-enabled solutions, including contract intelligence, workflow automation, supplier intelligence, and semantic search.
- Build and manage project plans, critical paths, timelines, milestones, dependencies, risks, issues, and actions across all active workstreams.
- Coordinate activities across business, technology, data, operations, and external vendor teams to maintain alignment and delivery momentum.
- Drive governance activities: stakeholder reviews, design decisions, approvals, and internal sign\-off processes.
- Support implementation of semantic data capabilities and AI\-ready data foundations required to scale AI solutions.
- Facilitate project meetings, steering forums, and working sessions; ensure clear ownership of decisions and actions.
- Prepare status reports, implementation plans, and executive communications for leadership stakeholders.
- Coordinate testing, deployment, business readiness, training, and transition\-to\-operations activities.
Identify and remove delivery blockers; escalate where required to keep initiatives on track.
Qualifications \& Skills
- 5\+ years of project management, implementation, or delivery experience in technology, digital transformation, automation, data, or AI\-related programs.
- Proven experience delivering at least one AI, automation, or enterprise digital transformation program end\-to\-end, from planning through deployment and adoption.
- Experience managing complex cross\-functional initiatives involving business, technology, data, operations, and external vendors.
- Demonstrated ability to coordinate software vendors, implementation partners, and third\-party development teams toward successful outcomes.
- Experience managing project governance, critical paths, dependencies, risks, and executive stakeholder communications.
- Proven ability to operate effectively in global, matrixed, multinational environments across multiple functions, geographies, and time zones.
- Strong communication, stakeholder management, and delivery leadership skills.
- Exposure to AI\-enabled capabilities such as intelligent intake, workflow automation, contract intelligence, semantic search, or knowledge management.
- Understanding of data, metadata, taxonomy, or AI\-ready data foundations.
- Bachelor's degree in Business, Technology, Information Systems, Engineering, Data, or a related field.
What We Offer (W\-2 Employment)
- Health, Dental, and Vision Insurance: Comprehensive coverage to support your overall well\-being.
- Employer Contributions to Health Savings Accounts (HSA): For employees enrolled in High Deductible Health Plans, Concord contributes toward your HSA.
- Flexible Spending Accounts (FSA): Options for healthcare and dependent care expenses, plus a Lifestyle Spending Account (LSA) for wellness and personal development.
- Disability Insurance: Short\-term (up to 12 weeks) and long\-term coverage, fully paid by the employer.
- Life and AD\&D Insurance: Employer\-provided coverage with options for additional voluntary coverage.
- Employee Assistance Program (EAP): Support for legal, financial, and personal challenges.
- 401(k) Retirement Savings: 1% employer match.
- Career Growth Opportunities: Pathways for advancement and skill development.
- Team Engagement Activities: Regular team\-building events and company\-sponsored activities to foster collaboration and connection.
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Concord is an execution partner helping organizations drive digital transformation, modernization, and scalable technology solutions. We deliver results that solve real business challenges. We operate globally and are growing fast, shaping the future of technology. Join a team trusted by top companies to drive strategic growth and operational excellence!
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Salary Context
This $93K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Concord USA, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($114K) sits 37% below the category median. Disclosed range: $93K to $135K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Concord USA AI Hiring
Concord USA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in TX, US. Compensation range: $135K - $135K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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