Top Artificial Intelligence Jobs

Design, deploy, and optimize LLM platform services including gateways, model routing, workflow orchestration, integrations, and prompt evaluation frameworks. Build reliable AI and agent integrations with enterprise systems, test and debug services under production-like loads, monitor performance and costs, and maintain platform SLOs. Collaborate with AI, software, and architecture teams, document technical solutions, support compliance validation, and participate in on-call rotations.

Deploy and implement Vocca’s first US healthcare clients, configuring accounts, managing integrations, and bringing practices live. Own post-sale support, customer success, account management, retention, and satisfaction while sales focuses on new business. Build a scalable US onboarding and operations playbook from scratch, document processes, reduce time-to-go-live, and help deploy approximately $1M in ARR during the first year.

Deploy Vocca’s AI voice-agent product for US healthcare clients, configuring integrations, managing go-lives, and owning post-sale success and account management. Initially hands-on, the role then develops the US implementation playbook, hires and leads a deployment team, and establishes product feedback loops. The role requires autonomous startup experience, technical product configuration skills, commercial credibility with healthcare and IT stakeholders, and the ability to scale a new operations function.

Lead enterprise AI tooling strategy, governance, roadmap, and lifecycle. Maintain an enterprise inventory of AI tools, manage vendor relationships, drive tool evaluation/onboarding/retirement, ensure security and Responsible AI alignment, and enable cross-functional adoption and optimization across engineering, product, security, procurement, and governance teams.

Leads scalable simulation-data systems for robotic motion AI, including synthetic data generation, domain randomization, recovery scenarios, curriculum design, trajectory replay, and dataset reproducibility. Develops and evaluates machine-learning models for safety-critical robotic systems using physics-based simulators, diagnoses failures quantitatively, and validates models on physical robots. Collaborates with foundation model, policy, testing, regulatory, product, and clinical teams.

Leads the architecture, development, and production deployment of AI agents and cloud-native platforms on GCP. Builds solutions with LangChain, LangGraph, RAG, prompt and context engineering, MLOps, observability, and scalable microservices. Oversees engineering metrics, DevSecOps, FinOps, troubleshooting, technical strategy, documentation, agile delivery, and stakeholder presentations. Manages and mentors cross-functional software, quality, and reliability engineering teams while driving reliable, efficient, production-ready AI systems.

Design, test, optimize, deploy, and operationalize AI/ML models across cloud, edge, and hybrid environments. Lead architecture selection, deployment pipelines, MLOps workflows, feature engineering, fine-tuning, retraining, and model monitoring. Collaborate with data, cloud, network, and security teams to integrate reliable models with mission systems, detect drift and performance degradation, and guide AI/ML lifecycle strategy for mission-critical applications.

Support and enforce enterprise data governance policies and tooling (Collibra, Ataccama). Maintain data quality, lineage, and compliance; translate steward rules into technical DQ rules; build SQL/Python solutions, dashboards, and integrations; define Jira work and collaborate with stewards, tech teams, and Data Quality squads to enable self-service governance and reporting.

Own and mature enterprise data products while leading AI enablement initiatives from concept through delivery and adoption. Responsibilities include defining product vision and roadmaps, prioritizing Agile backlogs, writing epics and user stories, serving as Product Owner for Agile squads, researching AI and data trends, conducting proofs of concept, and aligning technology, business, architecture, and governance stakeholders. Key capabilities include semantic models, knowledge graphs, agentic workflows, and Snowflake-based AI enablement.

Design, build, test, and deploy Retrieval-Augmented Generation (RAG) and agentic AI systems. Perform prompt engineering, data preprocessing, feature engineering, model evaluation and tuning. Integrate and optimize AI solutions on AWS (SageMaker, Bedrock, Lambda, S3), write maintainable Python code, troubleshoot in a mono-repo, and document models and workflows while collaborating cross-functionally.

Lead long-term product strategy for AI-driven outreach and appointment rescheduling capabilities. Architect conversational AI orchestration across voice and messaging channels, establish evaluation frameworks, define roadmaps and KPIs, analyze market and technology trends, and influence senior stakeholders across departments. The role engages clients and vendors, integrates complex datasets, guides strategic investment decisions, and mentors product talent while advancing healthcare delivery innovation.

Leads organization-wide AI enablement, opportunity discovery, intake, and backlog prioritization. Builds reusable AI project spaces, prompt libraries, workflow examples, training programs, and adoption feedback loops. Translates stakeholder needs into structured briefs with measurable outcomes, coordinates engineering handoffs, identifies reusable solution patterns, and communicates portfolio progress, ROI, reliability, and priorities to executives and departments.

Operate and scale production LLM and AI agent platforms across the enterprise. Build evaluation frameworks, observability, deployment safeguards, cost optimization, and governance capabilities. Partner with business and technology leaders to identify high-value AI opportunities, design future-state workflows, define requirements and success metrics, guide implementation, and drive adoption. Ensure AI solutions are reliable, measurable, compliant, and integrated into everyday operations.

Lead design, development, deployment, and maintenance of large-scale Python-based generative AI platforms and services. Collaborate with data science, technical, and business stakeholders to define architecture, deliver production APIs, enforce Python standards and testing, drive CI/CD and monitoring, mentor developers, manage vendor partnerships, and ensure governance, risk, and controls alignment for enterprise GenAI solutions.

Architect and implement scalable AI/ML platforms, pipelines, services, and APIs for an enterprise modernization program. Responsibilities include model training and deployment, MLOps, cloud infrastructure, multi-tenant and multi-region architecture, monitoring, optimization, responsible AI, documentation, dashboards, and third-party ML tool integration. The role guides agile teams while ensuring federal security, privacy, governance, compliance, performance, availability, and cost-efficiency requirements are met.

Leads enterprise AI platform strategy, architecture, enablement, governance, FinOps, vendor relationships, and executive communication. Evaluates AI capabilities across cloud, productivity, and model providers; builds multi-year roadmaps; embeds AI across platform organizations; establishes risk, security, compliance, and cost frameworks; and drives adoption through influence across a large matrixed engineering organization.

The Solutions Managing Director in AI Tech Strategy & Consulting drives AI strategy engagements for enterprise clients, manages high-value portfolios, and leads consulting teams while fostering C-suite relationships and business growth.

Designs, develops, and scales enterprise process automation, custom applications, APIs, integrations, AI agents, and intelligent workflows. Partners with architects and business stakeholders to translate requirements into scalable Microsoft-based solutions. Responsibilities include integrating ERP, CRM, CPQ, databases, and cloud platforms; troubleshooting and optimizing software; participating in architecture reviews; documenting solutions; evaluating emerging technologies; and supporting governance, security, and compliance.

Lead prompt engineering for LLM-based solutions: design, test, and refine prompts; collaborate with domain experts and developers; integrate LLMs into applications; build a scalable prompt framework and knowledge base; analyze user feedback and guide tool/platform selection.

Develop and optimize LLM prompts, build scalable prompt-engineering frameworks, integrate AI solutions into applications, analyze user feedback, document best practices, and collaborate with domain experts and software developers. The role also contributes to LLM technology and platform decisions across GE Vernova business units.

Design, develop, and optimize LLM prompts and a reusable prompt-engineering framework. Collaborate with domain experts and developers to integrate LLM solutions, collect user feedback, document best practices, and advise on tool/platform selection to improve business processes.

Design and optimize prompts for LLMs, collaborate with domain experts to translate business needs, integrate LLM solutions into applications, build a scalable prompt-engineering framework, gather and analyze user feedback, document best practices, and evaluate LLM tools and platforms to improve prompt effectiveness and reuse across business units.

Owns technical program management for AI infrastructure resources, including prioritizing GPU and compute allocation, overseeing data pipelines, coordinating data operations, and managing vendor-supported annotation and QA. Partners with engineering leaders and executives to develop resourcing roadmaps, mitigate compute and data risks, improve utilization processes, and support AI robotics initiatives. The role also manages data procurement and contract negotiations while driving cross-functional program deliverables.

Lead AI Engineer driving AI-assisted engineering adoption, defining reference architectures, conducting reviews, building LLM-powered agents and developer tools, improving CI/CD and security practices, mentoring engineers, and enabling cross-team engineering excellence across Output Solutions.

The AI Product Manager identifies and prioritizes AI opportunities, translates client and business problems into testable hypotheses, and designs experiments to validate value before investment. They balance discovery and delivery, define success metrics, assess AI autonomy and human oversight, and collaborate with technology, data, security, risk, business, and product teams. The role also develops reusable practices for responsible AI adoption and drives organizational learning without formal authority.