Jobs at MARLOWE COMPANIES INC.

Manage and grow staffing-services client accounts, lead new-client onboarding, generate and qualify business opportunities, nurture sales pipelines, support lead transfers, and provide consultative outreach. The role also includes regional client-engagement leadership, team mentorship, performance management, and market intelligence focused on infrastructure-related services.

Supports responsible AI governance by developing policies, maintaining control frameworks, conducting AI risk assessments, monitoring regulatory compliance, preparing reports, and advising legal, compliance, security, AI, and business stakeholders. The role also tracks governance metrics, supports audits, researches emerging regulations, and recommends improvements to AI governance programs.

Monitors and improves the performance, reliability, and adoption of AI-powered systems. Analyzes operational metrics, develops dashboards and reports, identifies bottlenecks, supports incident resolution, documents procedures, and recommends process improvements. The role collaborates with AI engineers, product teams, business stakeholders, and operations leaders while supporting governance, compliance, risk mitigation, and continuous improvement initiatives.

Design, develop, deploy, and optimize generative AI applications using LLMs and AI frameworks. Build scalable APIs, integrate foundation models and third-party platforms, automate business workflows, and connect AI solutions to internal systems and data sources. Monitor model quality, improve prompts and configurations, support responsible AI governance and security, evaluate emerging technologies, and collaborate with product, engineering, and business teams.

Designs, implements, and optimizes AI-powered workflow automation solutions. Responsibilities include analyzing business processes, documenting current and future-state workflows, identifying automation opportunities, gathering requirements, integrating automation with business systems, supporting testing and deployment, monitoring performance metrics, troubleshooting issues, training users, and promoting automation best practices and continuous improvement.

Leads global BPO business development and sales strategy to drive revenue growth. Responsibilities include developing sales strategies, managing remote sales teams across regions, securing new BPO and call center clients, building enterprise relationships, collaborating with marketing and operations, analyzing market trends, and representing the company at industry events and client meetings.

Leads the sales organization by developing strategic sales plans, growing revenue and market share, managing and mentoring geographically dispersed teams, building executive client relationships, optimizing sales processes, monitoring KPIs, overseeing budgets, and collaborating cross-functionally to achieve company objectives.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product requirements, priorities, success metrics, and business outcomes while coordinating engineering, AI, executives, and business stakeholders. Evaluates emerging AI technologies, supports responsible AI governance, manages feedback and adoption, and drives continuous product improvement.

Leads the architecture, development, optimization, deployment, and lifecycle management of enterprise machine learning systems. Establishes AI engineering standards, MLOps practices, data quality processes, model governance, security, and validation frameworks. Provides technical leadership, mentors engineers, evaluates emerging technologies, and supports AI transformation initiatives while partnering with data scientists, engineering, product, and business teams.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct prompt experiments, improve response quality, maintain prompt libraries, document standards, and support governance. Collaborate with AI engineers, trainers, product teams, and business stakeholders to translate requirements into prompting strategies and support Generative AI implementations. Research emerging techniques, evaluate tools, and recommend improvements to AI quality and effectiveness.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Develop and execute staffing sales strategies, build and manage client relationships, generate and qualify leads, develop business pipelines, schedule prospect appointments, close deals, and coordinate qualified leads with internal teams. The role also provides client staffing recommendations, oversees regional sales initiatives, and mentors direct reports through performance management.

Lead the organization’s AI workflow automation strategy, identify and prioritize automation opportunities, design scalable AI-powered workflows, and manage initiatives from concept through implementation. Partner with business and technical stakeholders, track budgets and timelines, establish governance, measure ROI and operational outcomes, develop performance dashboards, evaluate emerging technologies, and drive continuous improvement, adoption, and organizational transformation.

Labels, categorizes, validates, and quality-checks structured and unstructured data used to train AI and machine learning models. Maintains dataset accuracy, consistency, documentation, and version control while following detailed annotation guidelines. Collaborates with AI engineers, data scientists, and quality teams, identifies errors, provides process feedback, and supports continuous improvement of annotation workflows.

Designs and maintains Retrieval-Augmented Generation systems, including document ingestion, indexing, semantic search, vector databases, embedding pipelines, and LLM integrations. Develops APIs and scalable knowledge systems, optimizes retrieval accuracy and performance, monitors production environments, and implements governance and security controls. The role also evaluates emerging retrieval technologies, troubleshoots indexing issues, documents procedures, and collaborates with AI engineers and product teams.

Reviews AI-generated content for accuracy, relevance, quality, safety, compliance, and adherence to organizational standards. Identifies factual inconsistencies, bias, harmful outputs, and recurring issues; documents findings and escalates concerns. Provides structured feedback to AI trainers and prompt engineers, tracks quality metrics, prepares reports, supports root-cause analysis, and helps improve prompts, training processes, and review guidelines.

Leads the design, development, and optimization of enterprise Retrieval-Augmented Generation platforms. Responsibilities include building retrieval architectures, ingestion and indexing frameworks, semantic and vector search systems, evaluation methods, scalable APIs, and LLM integrations. The role also covers security, governance, compliance, monitoring, troubleshooting, documentation, technical leadership, mentoring, and evaluating emerging AI retrieval technologies.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering and evaluation strategies, and ensures secure, scalable production deployments. Partners with executives and business leaders to define AI roadmaps, mentors engineering teams, evaluates emerging technologies, and drives proof-of-concept initiatives and responsible AI practices.

Develop and manage the strategy, roadmap, lifecycle, and delivery of AI-powered products. Translate business needs into product requirements, collaborate with engineering and AI teams, define success metrics, monitor adoption and ROI, and drive continuous improvements. The role also engages customers and stakeholders, evaluates emerging technologies, supports governance and compliance, and promotes responsible AI practices.

Designs, tests, evaluates, and refines prompts for generative AI applications across customer support, content generation, workflow automation, knowledge management, and productivity use cases. The role evaluates AI outputs for accuracy, relevance, tone, and consistency; documents testing results; maintains prompt libraries and guidelines; collaborates with AI engineers, product teams, data scientists, and business stakeholders; and researches emerging AI tools and prompting techniques.