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About This Role
Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
Director / Client Partner - Information Services, Enterprise AI
Remote – United States
Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
Scope of the Role
The Information Services industry is undergoing rapid transformation as organizations look to leverage Artificial Intelligence to modernize knowledge management, content operations, research workflows, and customer experiences. Leading publishers, information providers, legal and regulatory organizations, and research institutions are investing heavily in Enterprise AI initiatives to unlock the value of their content, data, and expertise.
Innodata is at the forefront of that transformation. We are seeking a Director / Client Partner to drive growth and deepen strategic relationships within the Information Services sector. This is a senior leadership role focused on expanding revenue, identifying new opportunities, and building executive-level partnerships with some of the world's leading information and knowledge organizations.
You will own account strategy, business development, executive engagement, and revenue growth across a portfolio of strategic accounts. Working closely with Innodata's AI, engineering, and delivery teams, you will help customers design and implement Enterprise AI solutions that drive measurable business outcomes. Your success will be measured by your ability to grow existing relationships, create new opportunities, and establish Innodata as a trusted AI transformation partner within the Information Services ecosystem.
What You'll Own:
- Own the overall account strategy, growth plan, pipeline, bookings, forecasting, and revenue performance for strategic Information Services customers.
- Develop and execute account expansion strategies that uncover new opportunities across publishing, research, legal, regulatory, and information services organizations.
- Build and maintain executive-level relationships with key stakeholders across business, technology, content, research, and operations functions.
- Identify, qualify, and close complex Enterprise AI, data engineering, digital transformation, and managed services engagements.
- Drive expansion into new AI initiatives including Generative AI, Agentic AI, enterprise search, knowledge management, document intelligence, and workflow automation.
- Collaborate with solution architects, AI specialists, engineering leaders, and delivery teams to shape customer solutions and transformation programs.
- Position and cross-sell Innodata's capabilities across AI-powered data engineering, document processing, GenBI, enterprise knowledge solutions, RAG and context engineering, AI evaluation, HITL workflows, and managed AI operations.
- Lead commercial negotiations, proposal development, opportunity qualification, and contract discussions with senior stakeholders.
- Build a multi-threaded account presence across technical, business, procurement, and executive teams to accelerate growth and strengthen strategic partnerships.
- Serve as the voice of the customer internally, helping shape Innodata's Enterprise AI offerings and long-term go-to-market strategy.
You'll Thrive in This Role If You Have:
- 10+ years of experience in enterprise technology, AI/ML, information services, content technologies, data engineering, cloud solutions, digital transformation, or strategic account management.
- 5+ years of experience managing and growing large strategic accounts within Information Services, Publishing, Research, Legal, Regulatory, or knowledge-driven organizations.
- A demonstrated track record of expanding enterprise relationships and closing complex services engagements valued at $2M+.
- Experience selling AI, data engineering, content modernization, enterprise search, knowledge management, analytics, cloud, digital transformation, or managed services solutions.
- Strong executive presence and the ability to engage senior leaders across business and technical organizations.
- Expertise in account planning, opportunity qualification, forecasting, and complex commercial negotiations.
- The ability to navigate multiple stakeholder groups including Product, Technology, AI/ML, Content Operations, Research, Data, Procurement, and executive leadership.
- A Bachelor's degree is required; an MBA or Master's degree is preferred.
Preferred Qualifications:
- Existing relationships within leading Information Services, Publishing, Legal, Regulatory, Research, or Professional Information organizations.
- Prior experience selling AI, engineering, consulting, content technology, or managed services solutions into Information Services enterprises.
- Working knowledge of Enterprise AI technologies including RAG architectures, enterprise search, document intelligence, knowledge management platforms, and workflow automation solutions.
- Experience with Agentic AI, Generative AI, AI evaluation frameworks, data engineering programs, or managed AI operations.
- Familiarity with modern cloud and data ecosystems including AWS, Azure, Google Cloud Platform (GCP), Databricks, Snowflake, and related technologies.
- Experience converting existing delivery engagements into larger transformation programs and recurring managed-services revenue streams.
The expected salary range for this position is $170,000 - $190,000 USD p/year, based on experience, skills, and qualifications.
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission's guide at https://consumer.ftc.gov/articles/job-scams.
If you believe you've been targeted by a recruitment scam, please report it to Innodata at [email protected] and consider reporting it to the FTC at ReportFraud.ftc.gov*.*
Salary Context
This $170K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Innodata, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $170K to $190K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Innodata AI Hiring
Innodata has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Ridgefield Park, NJ, US. Compensation range: $85K - $220K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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