As Head of AI Platform & Enablement, you will own enterprise AI strategy and delivery for Bullish Group, driving employee adoption, partnering with business leaders to improve workflows, and leading the development of shared AI capabilities.
Compensation
The anticipated base salary for this position is $275,000 - $340,000 + discretionary annual target bonus + performance incentives/benefits.
Responsibilities
Own enterprise AI strategy for Bullish Group.
Identify the most valuable opportunities across business functions, set priorities, and take responsibility for delivery.
Build and lead the AI Platform & Enablement team.
Partner with recruiting to hire the team.
Own the AI capability roadmap and lead development of shared services, including agents, connectors, a skills library, and discovery tools.
Lead the vision and execution of low‑code agent building across autonomous and conversational agents, shaping product strategy, design, and customer delivery.
Responsibilities
Set the multi‑year vision for low‑code agent building on UiPath
Own Agent Builder end to end, defining goals, instructions, tools, context, outputs, testing, debugging, and publishing
Shape Autopilot’s agent‑building experience and partner with governance, evals, and ML teams
Lead Agent Reviewer to assess agent readiness and suggest fixes
Design shared capabilities (tools, context, memory, guardrails, evals) within the builder
Requirements
Nice to have: startup or founding PM experience
Nice to have: front‑end or product design experience
Nice to have: experience with hyperscalers or automation vendors
Lead product direction for NeMo Relay, an open-source library for AI agent observability and control.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 168,000 USD – 258,750 USD for Level 4; and 208,000 USD – 327,750 USD for Level 5.
Responsibilities
Take charge of vision, strategy, and roadmap for NeMo Relay
Drive adoption through integrations with agent frameworks and vendors
Partner with teams behind NeMo Guardrails, NeMo Fabric, NeMo Compass and OpenShell
Turn community feedback into product decisions
Plan releases and communicate roadmap
Requirements
BS or MS in Computer Science, Electrical Engineering, or equivalent experience
Lead technical strategy and solution architecture for enterprise customers, integrating simulation, digital engineering, and AI workflows.
Compensation
Base Salary Range: $160000 - $240000
In addition to the base salary, this role may be eligible for an annual bonus, equity, and other discretionary bonuses. Synopsys offers comprehensive health, wellness, and financial benefits as part of a competitive total rewards package. The actual compensation offered will be based on a number of job-related factors, including location, skills, experience, and education. Your recruiter can share more specific details on the total rewards package upon request. The base salary range for this role is across the U.S.
Responsibilities
Act as a trusted technical advisor to executive, engineering, and IT stakeholders at strategic and enterprise customer accounts.
Partner with Sales, account teams, and regional leadership to identify high-value opportunities.
Lead customer discovery workshops and ideation sessions.
Define and document solution vision, roadmap, value drivers, risks, and adoption strategies.
As a Engineer II, Google Cloud you will contribute to the design, build, testing, and optimization of cloud-native applications on Google Cloud, supporting work across the full software development lifecycle from new product ideas to modernization efforts.
Compensation
The salary range for this position is: $61,000 - $100,000. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus.
Responsibilities
Building cloud-native applications on Google Cloud across the software development lifecycle
Developing generative AI solutions using Google Vertex AI, LLM APIs, and Python
Designing prompt engineering, context engineering, and retrieval-augmented generation workflows
Testing application functionality, model behavior, and system performance across delivery stages
Applying agent orchestration, LangChain, LangGraph, and vector database methods to solution design
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