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About This Role
Others, New Jersey
Job Summary
Agentic Consulting: Gemini Enterprise .
This role blends strategic business consulting, industry\-specific domain expertise, and deep technical execution utilizing Google Cloud’s Gemini Enterprise Agent Platform (the advanced ecosystem unifying what was previously Vertex AI and enterprise agent tooling).
Position Overview
As an Agentic Consulting Specialist / Product Manager , you will bridge the gap between high\-level business strategy and technical implementation. Your primary focus will be designing, building, and deploying autonomous AI Agents that automate complex, multi\-step, multi\-app workflows. You will serve as the trusted advisor to both C\-suite business stakeholders and IT engineering teams, ensuring that Agentic AI solutions solve real\-world industry pain points.
Key Responsibilities
1\. AI Product Strategy and Vision (Consulting \& Advisory) You will drive the adoption of Agentic AI by demonstrating how autonomous agents can shift an organization from reactive software usage to proactive, automated process execution. Domain\-Specific Solutioning: Apply deep industry knowledge to pinpoint high\-value automation use cases: Financial Services (FS): Automating credit risk reporting, compliance checks, or fraud investigation workflows. Retail \& Consumer Packaged Goods (RCPG): Real\-time supply chain adjustments, automated inventory routing, or predictive marketing campaign launches. Life Sciences \& Healthcare (LSH): Accelerating clinical trial documentation search, patient intake routing, or medical literature synthesis. Manufacturing \& Technology: Email\-based order processing automation, predictive asset maintenance workflows, or software development lifecycle (SDLC) speed\-ups. Google Cloud Architecture Alignment: Consult clients on utilizing the Gemini Enterprise Agent Platform to replace traditional, rigid chatbots with dynamic, multi\-agent frameworks. Agile Discovery: Facilitate workshops with business and IT stakeholders to evaluate business processes, estimate ROI, and map out the vision for an "agentic taskforce." 2\. Product Development and Execution (Product Management) You will own the lifecycle of the AI agent from conceptualization to stable production deployment, managing cross\-functional technical teams. Roadmap \& Backlog Management: Qualify agent use cases based on technical feasibility and business impact. Maintain the product backlog using Agile methodologies. Technical \& Functional Specifications: Translate complex business rules into concrete logic definitions. You will write specifications outlining: Agent Logic and Reasoning: Defining paths using Agent Studio (for low\-code/no\-code workflows) or specifying requirements for the Agent Development Kit (ADK) (for developer code\-first graph\-based sub\-agent networks). Data Grounding: Defining how agents safely connect to enterprise data sources (e.g., BigQuery, Google Workspace, or third\-party CRM/ERP systems) using secure connectors. Integrations: Utilizing the Agent2Agent (A2A) protocol to ensure a Google agent can seamlessly hand off tasks to partner agents (e.g., Salesforce, Workday, ServiceNow). Guardrails \& Quality Assurance: Define functional requirements for safety and performance. Work with engineers to utilize Agent Simulation and Agent Evaluation tools to test agents against synthetic user profiles and prevent prompt injection vulnerabilities via Model Armor.
Skill Requirements
3\. Stakeholder Alignment and Go\-to\-Market (GTM)
An agent is only valuable if it is trusted and adopted. You will act as the ultimate product evangelist and governance lead.
Cross\-Functional Orchestration: Serve as the central hub connecting Engineering, User Experience (UX), Sales, and Marketing teams to ensure a smooth product rollout.
Deployment and Enterprise Governance: Collaborate with enterprise IT admins to ensure agents are securely deployed into the organization's Gemini Enterprise app hub. Ensure every custom agent is registered under an Agent Identity and governed via the central Agent Registry .
Feedback Loops \& Iteration: Gather direct user feedback from client teams, analyze Agent Observability traces to see how the agent reasons through its tasks, and continuously refine prompts, tools, and workflows to improve completion rates.
KPI Tracking: Monitor and present product performance metrics, such as:
Task Success Rate (percentage of workflows completed without human intervention).
Time\-to\-Resolution Reduction (e.g., reducing email processing times from hours to real\-time).
API \& Compute Cost Efficiency (tracking vCPU and token spend).
Maximum Salary (US): 170000
Minimum Salary (US): 140000
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Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At HCLTech, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
HCLTech AI Hiring
HCLTech has 5 open AI roles right now. They're hiring across AI Product Manager, Data Scientist, AI/ML Engineer. Positions span US, St. Louis, MO, US, San Antonio, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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