AI Jobs in South River, NJ

Prototypes or demos do not measure success in this role; it’s measured by operational leverage: faster workflows, fewer manual processes, lower costs, and teams that can scale far more efficiently because of the systems you deployed.

• Embed within operational teams across the company, underwriting, claims, premium finance, patient experience, broker operations, sales engineering, and more, and work directly with them to understand how decisions are made, where work slows down, and what can be automated.

li>Research practical automation technologies including robotic arms, sensors, machine vision systems, label applicators, and compact conveyor solutions.

The disclosed range takes into account the wide range of factors that are considered in making compensation decisions including, but not limited to, geographic location, relevant education, qualifications, certifications, experience, skills, seniority, performance, sales or revenue-based metrics, and business or organizational needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

Bachelor's Degree in relevant field (e.g., Liberal Arts, Business Administration, Management, Computer Science) AND 8+ years experience in financial management, business planning, operations management, project management, or business-related roles. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $165,600 - $303,600 per year.

You'll build and maintain the core infrastructure, CI/CD pipelines, and platform services that underpin our machine learning initiatives and go further in designing the harnesses, sandboxes, and evaluation frameworks that let AI agents be developed, tested, and trusted in production. Implement evaluation frameworks for agent behavior using a combination of vendor , open source or in house built tools - covering task success, tool selection accuracy, trajectory evaluation, hallucination rates, latency, and cost.

Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/ .

As a Lead Software Engineer on our AI Platform Team, you will drive the design and development of the next generation of agentic, reliable, and scalable GenAI platforms supporting state-of-the-art large language models, evaluation systems, and secure developer tooling.

li>Architect and implement AI‑powered solutions using Snowflake Cortex including Cortex LLM functions, Cortex Search for semantic/vector/RAG capabilities, Cortex Analyst for conversational analytics, Document AI, and integration with external LLMs (e.g., for fine‑tuning, agents, and multimodal data processing).

• Lead end‑to‑end data architecture and solution design for Snowflake‑based data platforms including logical/physical data models, ingestion patterns (batch, streaming, Snowpipe), storage layers (raw, curated, consumption/semantic), and consumption patterns for analytics, BI, and AI/ML.

Youll collaborate closely with engineering leaders and platform teams to accelerate Dynamics 365, Power Platform, and AI through hands-on engagements like Proof of Concepts (PoC), hackathons, and architecture workshops. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $112,000 - $185,300 per year.

It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini's discretion, consistent with applicable laws.