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About This Role
POSITION 1
AI Program Lead
Location: Chicago, IL
Experience Required: 6\-8
Number of Positions : 3
Name Required Experience
AI \& Gen AI \- Products \& Tools Yes \>7 years
Job Description: 3\. JD – Program Lead (AI Tooling \& AI DLC Enablement)
Role Title
AI Program Lead – Tooling, AI DLC \& Solution Architecture
Role Summary
Act as the single\-threaded leader responsible for driving:
· AI tooling strategy
· AI Development Lifecycle (AI DLC)
· Jellyfish and Amplitude teams management
· Proposal and solution definition
This role is not just coordination — it must actively shape solution architecture, define scope, and drive execution.
Key Responsibilities
· Lead end\-to\-end solution definition and proposal development
· Define:
o AI Development Lifecycle (AI DLC) frameworks
o Intake processes and artifacts
o End\-to\-end AI delivery lifecycle
· Drive alignment across:
o AI DLC
o Quality Engineering (AIQE)
o App Ops / Observability
· Own development of:
o Solution architecture
o Delivery roadmap
o Execution model
· Coordinate and manage:
o Jellyfish and Amplitude teams
o Engineering and product stakeholders
· Facilitate working sessions with stakeholders to:
o Refine scope
o Finalize vision
· Ensure:
o Clear articulation of “what will be built” (key client gap)
· Drive delivery readiness and execution planning
Required Skills
· Strong experience in:
o Program leadership (AI, Data or Digital programs)
o Solution architecture and proposal building
· Deep understanding of:
o AI/ML lifecycle
o Engineering workflows
· Proven ability to:
o Define large\-scale transformation programs
· Strong stakeholder engagement at leadership level
Preferred Skills
· Experience in:
o AI factory frameworks
o Agent\-based / LLM\-based systems
· Exposure to:
o Observability, analytics, and tooling ecosystems
Profile Expectation (Critical per Customer)
· Fully dedicated resource (not part\-time)
· Ability to work in intense collaboration (even evenings/weekends if needed)
· Strong ownership mindset (not staff augmentation mindset)
· Capable of bridging gap between:
o Vision
o Architecture
o Execution
Role Descriptions: Lead end\-to\-end solution definition and proposal developmentDefine:oAI Development Lifecycle (AI DLC) frameworksoIntake processes and artifactsoEnd\-to\-end AI delivery lifecycle \[TCS Discus...ng and SoW \| Meeting]Drive alignment across:oAI DLCoQuality Engineering (AIQE)oApp Ops / Observability \[TCS Discus...ng and SoW \| Meeting]Own development of:oSolution architectureoDelivery roadmapoExecution modelCoordinate and manage:oJellyfish and Amplitude teamsoEngineering and product stakeholdersFacilitate working sessions with stakeholders to:oRefine scopeoFinalize visionEnsure:oClear articulation of what will be built (key client gap) \[TCS Discus...ng and SoW \| Meeting]Drive delivery readiness and execution planning
Essential Skills: Act as the single\-threaded leader responsible for driving:AI tooling strategyAI Development Lifecycle (AI DLC)Jellyfish and Amplitude teams managementProposal and solution definition
Desirable Skills:
Keyword:
Skills: AI \& Gen AI \- Products \& Tools
Experience Required: 6\-8
POSITION 2
Role: Interim Consulting Lead
Location: Chicago, IL Minneapolis, MN
Experience Required: 8\-10
Number of Positions : 3
Name Required Experience
AI and Automation Yes \>7 years
Interim Consulting Lead
Purpose
Provide temporary leadership across one or more of the four core functions until permanent internal leaders are hired, while accelerating framework definition and capability build\-out.
Key responsibilities
- Serve as a consulting lead across delivery, QA, synthetic data, or platform functions during the early build phase.
- Help define the framework, processes, and practical implementation patterns that the permanent leaders will inherit.
- Operate as a highly engaged on\-site partner rather than a remote advisor or slide\-focused consultant.
- Support hiring, interviewing, and early team formation while ensuring momentum is not lost because of slow HR processes.
- Transfer knowledge to internal leaders and teams so the model remains scalable and does not create long\-term consulting dependency.
Role requirements
- Proven experience standing up delivery and governance capabilities in new AI environments.
- Ability to move quickly, work collaboratively, and operate under close executive scrutiny.
- Strong practitioner mindset, with both strategic and operational depth.
- Ability to communicate, challenge assumptions, and actively participate in design decisions.
Role Descriptions: Interim Consulting Lead
Skills: AI and Automation
Experience Required: 8\-10
Role: AI Ops Leader
Location: Chicago, IL\-Minneapolis, MN
Experience Required: 10\+
Number of Positions : 3
Name Required Experience
AI and Automation Yes \>7 years
Must Have Technical/Functional Skills
- Strong expertise in AI Ops / MLOps / LLM Ops practices
- End\-to\-end model lifecycle management
- Advanced model monitoring, observability, and alerting frameworks
- Drift detection, performance tracking, and automated retraining
- CI/CD and DevSecOps for AI/ML systems
- Scalable deployment architectures for AI/ML and LLM
- AI governance, risk, security, and compliance frameworks \[
- AI platform engineering and operational tooling experience
- Performance optimization (latency, cost, scalability) for AI workloads
- Strong experience in automation of AI operations workflows
- Data pipeline integration and ML infrastructure management
- Cross\-functional collaboration with engineering, data, and platform teams
Roles \& Responsibilities
- Define and implement AI Ops / MLOps / LLM Ops strategy for enterprise AI platforms
- Manage end\-to\-end AI operations lifecycle (deployment, monitoring, scaling, optimization)
- Establish model monitoring, observability, and alerting frameworks for production AI systems
- Implement model lifecycle management (versioning, deployment, retraining, rollback, drift detection)
- Define and track AI Ops KPIs (performance, reliability, incident reduction, automation efficiency)
- Ensure high availability, scalability, and performance of AI systems in production
- Drive adoption of CI/CD and DevSecOps practices for AI/ML systems
- Implement governance, risk, security, and compliance controls for AI systems
- Collaborate with AI engineering, data, and platform teams for seamless operation
- Manage incident response, root\-cause analysis, and continuous improvement for AI system
- Optimize cost, latency, and resource utilization of AI workloads
- Drive automation of AI operations processes and workflows
Pay: $80\.00 \- $90\.00 per hour
Work Location: In person
Salary Context
This $166K-$187K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Saransh, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $166K to $187K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Saransh AI Hiring
Saransh has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in North Chicago, IL, US. Compensation range: $187K - $187K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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