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United States
Onsite
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Associate Director, AI & Applied Solutions based in the United States.
The Associate Director, AI & Applied Solutions will lead the practical application of artificial intelligence, data, and data science to solve complex business challenges and deliver measurable client value.
This hands-on leadership role combines technical innovation, strategic consulting, and client engagement to develop advanced AI-powered applications, agents, and analytical solutions.
You will architect and prototype generative AI systems, explore emerging large language model technologies, and help shape the organization's AI technology ecosystem.
Working alongside senior executives, engagement leaders, and multidisciplinary teams, you will translate business priorities into scalable technical solutions.
You will also drive AI-enabled transformation initiatives focused on revenue growth, cost optimization, risk management, and improved customer and employee experiences.
Beyond client delivery, you will contribute to internal innovation, develop reusable technical assets, and mentor colleagues in applying AI effectively across consulting engagements.
You will combine technical execution, strategic problem-solving, and engagement leadership to deliver innovative AI and data solutions that generate tangible business outcomes. Your responsibilities will include:
Engagement Leadership
Partner with engagement leaders to design and deliver advanced quantitative and qualitative analyses across operational, strategic, and economic dimensions, incorporating AI and data science capabilities.
Serve as a primary client contact and day-to-day representative, leading project teams and building strong relationships with stakeholders across industries.
Apply structured, hypothesis-driven problem-solving methodologies and analytical frameworks to address complex business challenges.
Hands-On AI, Data & Data Science Delivery
Design, build, and demonstrate functional client prototypes, including AI-powered applications, agents, copilots, Retrieval-Augmented Generation (RAG) systems, and analytical tools.
Leverage modern AI development technologies such as Claude Code, Cursor, LangChain, LangGraph, vector databases, and agent frameworks.
Apply data engineering and DevOps practices, including reliable data pipelines, version control, CI/CD, evaluation, and monitoring, to ensure prototypes are repeatable and suitable for client engineering teams.
Execute applied data science initiatives, covering hypothesis development, model selection, evaluation, and deployment of machine learning and LLM-based solutions.
Translate technical analyses and AI-generated outputs into actionable business insights, strategic recommendations, and financial impact projections.
Technology Strategy & AI Innovation
Shape and continuously improve the organization's AI technology stack, covering models, frameworks, data platforms, orchestration, and DevOps/MLOps tools.
Research and implement emerging AI techniques, including agentic systems, multi-agent orchestration, RAG, fine-tuning, and evaluation frameworks.
Promote strong data engineering and DevOps foundations to support production-ready AI solutions, including reliable pipelines, data modeling, CI/CD, infrastructure, and observability.
Develop reusable intellectual property, including technical accelerators, reference architectures, prompt libraries, agent templates, and analytical frameworks.
Client Value & Business Performance
Deliver measurable business outcomes through AI, data, and data science initiatives focused on revenue growth, cost optimization, risk management, and customer and employee experience.
Establish KPIs to measure the effectiveness and business impact of AI-enabled transformation initiatives.
Translate complex technical concepts into clear, compelling business narratives and presentations for executive and board-level audiences.
Mentoring & AI Evangelism
Mentor colleagues across the organization on AI tools, coding assistants, agent development, and prompt engineering.
Support the professional development of junior team members in both technical delivery and consulting methodologies.
Promote the adoption of AI in consulting through internal knowledge sharing and external thought leadership.
Create high-quality presentations and live demonstrations tailored to audiences ranging from line managers to senior executives and board members.
The ideal candidate combines substantial consulting or strategic business experience with hands-on technical expertise in AI development, data science, and software engineering. The following qualifications and capabilities are expected:
Professional Experience
6–10 years of progressive professional experience.
Previous experience in top-tier management consulting, corporate strategy, investment banking, or private equity is strongly preferred.
Demonstrated experience supporting or leading AI, data, and analytics initiatives.
Hands-on experience building generative AI applications, intelligent agents, or LLM-powered prototypes deployed to real users is highly desirable.
Exposure to financial services, including property and casualty insurance, life insurance, banking, or wealth management, is an advantage.
Experience in retail, healthcare, or manufacturing environments is also beneficial.
Technical Expertise
Strong practical knowledge of generative AI development patterns, including LLM APIs, prompt engineering, RAG, agentic frameworks, tool/function calling, and model evaluation.
Ability to develop, read, and review code in Python and/or TypeScript.
Experience using AI-powered coding assistants such as Claude Code, Cursor, or GitHub Copilot.
Working knowledge of data engineering concepts, including ETL/ELT pipelines, data modeling, orchestration, batch and streaming architectures, and feature or vector stores.
Familiarity with data orchestration tools such as Airflow, dbt, or Dagster.
Understanding of DevOps and MLOps fundamentals, including Git, CI/CD, Docker, infrastructure as code, monitoring, AI evaluation, and observability.
Familiarity with cloud-based data and AI platforms such as Snowflake, Databricks, BigQuery, and AI services across AWS, Azure, or Google Cloud.
Strong understanding of core machine learning concepts and their practical business applications.
Leadership & Communication
Excellent written and verbal communication skills, with the ability to present complex ideas to senior executives and business stakeholders.
Confidence in demonstrating functional software solutions and explaining their technical and commercial value.
Strong leadership, mentoring, and stakeholder management capabilities.
Resilience, critical thinking, and adaptability when navigating ambiguous, fast-paced environments.
Comfort working directly with senior executives and managing high levels of client exposure.
Education & Other Qualifications
Bachelor's degree with strong academic credentials from a top-tier institution, preferably in Business, Economics, Engineering, Computer Science, Data Science, or another quantitative discipline.
Master's degree in Business Administration, Computer Science, Data Science, Statistics, Analytics, Operations, or a related field is strongly preferred.
Willingness and ability to travel multiple days per week, including occasional international assignments.
Annual starting base salary ranging from $140,000 to $190,000, depending on experience, qualifications, technical expertise, and organizational considerations.
Performance-based incentive compensation.
Competitive employee benefits package.
Opportunity to work on innovative AI and data science initiatives with measurable business impact.
Exposure to senior executives and clients across multiple industries.
Hands-on involvement in building and deploying advanced generative AI applications and agentic systems.
Opportunity to influence technology strategy, shape AI development practices, and build reusable technical solutions.
Collaborative, agile working environment with small consulting teams, typically consisting of 3–5 professionals.
Opportunities to mentor colleagues and contribute to the development of AI capabilities across the organization.
Potential exposure to international client projects and diverse business environments.
