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Consultative Offerings - Analyst - Data & AI Solutions Engineering
DeloitteNew York City, United Statesfull_timeVerifiedPosted 1 Aug 2026
💰 $100,000/yr
About the role
Consultative Offerings - Analyst - Data & AI Solutions Engineering
Same job available in 8 locations
Atlanta, Georgia, United States
Boston, Massachusetts, United States
Chicago, Illinois, United States
Dallas, Texas, United States
McLean, Virginia, United States
New York, New York, United States
Philadelphia, Pennsylvania, United States
San Francisco, California, United States
Position Summary
Are you ready to go beyond your potential and reach something greater? At Deloitte, we believe in more than just growth—we believe in exponential possibilities. Here, your unique talents and ambitions are amplified by the power of our collaborative teams, innovative thinking, and mentorship. When you join Deloitte, you don’t just build a career—you unlock unlimited opportunities, shaping your future and the world around you. Take the power of you and put it to the power of Deloitte. Reach your exponential!
Recruiting for this role ends on 08/07/2026.
Work You’ll Do
As a Data & AI Solutions Engineering Analyst, you won’t just write code or build dashboards—you’ll solve some of the most pressing business and societal challenges of today using data, intelligence, and creativity. We’re looking for individuals who think critically, act boldly, and are ready to take ownership in fast-moving environments.
You’ll work at the intersection of AI engineering, data infrastructure, and forward deployment, contributing to client solutions that go beyond analysis and into activation, building data-driven solutions with speed, scale, and substance. You won’t stop at insights — you’ll operationalize intelligence through platforms, automation, and integrated deployment. From designing agentic workflows that automate decision-making to building pipelines that fuel generative AI models, you’ll be empowered to think like an entrepreneur and deliver like an engineer.
- Build agentic workflows powered by AI that act autonomously with human oversight—helping clients automate, analyze, and adapt.
- Design and deploy generative-AI solutions, such as copilots, assistants, and intelligent content generation tools using large language models (LLMs).
- Serve as a Forward Deployed Engineer: embed with client teams to understand real-world challenges, co-create tailored AI solutions, and ensure production-grade implementation.
- Shape strategy and execution: You’ll do more than model—you’ll help clients transform how they operate with AI embedded in their core workflows.
- Translate business needs into technical architectures using cloud platforms, APIs, ML models, and modern DevOps tooling.
Key Responsibilities:
- Innovation and Critical Thinking
- Apply structured problem-solving and systems thinking to navigate ambiguity and uncover impactful insights.
- Challenge assumptions, propose new solutions, and take initiative in designing scalable AI/analytics systems.
- Generative AI& Applied Intelligence
- Develop and fine-tune generative AI models (e.g., LLMs, diffusion models) for use cases like summarization, reasoning, content generation, and conversational agents.
- Design prompt engineering workflows and retrieval-augmented generation (RAG) pipelines to support real-time decisioning and contextual understanding.
- Implement monitoring, evaluation, and governance frameworks for AI/ML models in production.
- Data Engineering & Infrastructure
- Build and optimize robust data pipelines using tools like SQL, Python, Apache Spark, Airflow, and cloud-native services.
- Design data architectures that support scale, real-time insights, and cross-functional AI workloads.
- Implement ELT/ETL processes that ensure data quality, lineage, and observability.
- Forward Deploy Engineering
- Operate in agentic client environments, embedding directly with client teams to rapidly prototype, iterate, and deploy solutions.
- Translate business challenges into technical specifications and lead solution delivery from concept to production.
- Interface directly with business and engineering stakeholders, driving impact in highly visible engagements.
- Collaboration & Communication
- Communicate complex technical topics clearly to non-technical stakeholders through compelling storytelling and visualization.
- Mentor peers and clients on analytics best practices and AI solution adoption, promoting a da
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