Consultative Offerings - Analyst - Data & AI Solutions Engineering

This role focuses on building and deploying AI solutions for clients, with a strong emphasis on agentic workflows, generative AI, and data engineering. The analyst will work at the intersection of AI engineering, data infrastructure, and client deployment, operationalizing intelligence and transforming business operations with embedded AI.

What you'd actually do

  1. Build agentic workflows powered by AI that act autonomously with human oversight—helping clients automate, analyze, and adapt.
  2. Design and deploy generative-AI solutions, such as copilots, assistants, and intelligent content generation tools using large language models (LLMs).
  3. 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.
  4. 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.
  5. Translate business needs into technical architectures using cloud platforms, APIs, ML models, and modern DevOps tooling.

Skills

Required

  • Python
  • SQL
  • Apache Spark
  • Airflow
  • cloud platforms
  • APIs
  • ML models
  • DevOps tooling
  • data pipelines
  • data architectures
  • ELT/ETL processes
  • problem-solving
  • critical thinking
  • collaboration
  • communication

Nice to have

  • Windows-based systems
  • Microsoft Excel
  • Microsoft Word
  • Microsoft PowerPoint
  • scripting languages
  • data visualization platforms

What the JD emphasized

  • agentic workflows
  • generative AI
  • large language models (LLMs)
  • Forward Deployed Engineer
  • AI embedded in their core workflows
  • cloud platforms
  • APIs
  • ML models
  • DevOps tooling
  • Generative AI
  • Applied Intelligence
  • generative AI models
  • LLMs
  • conversational agents
  • prompt engineering workflows
  • retrieval-augmented generation (RAG) pipelines
  • AI/ML models
  • data pipelines
  • Apache Spark
  • Airflow
  • cloud-native services
  • data architectures
  • AI workloads
  • ELT/ETL processes
  • data quality
  • observability
  • agentic client environments
  • solution delivery
  • business and engineering stakeholders
  • AI solution adoption
  • AI and data capabilities
  • Agentic AI thinking
  • Data engineering fluency
  • Forward deployed presence

Other signals

  • building data-driven solutions
  • operationalize intelligence
  • designing agentic workflows
  • building pipelines that fuel generative AI models
  • develop and fine-tune generative AI models
  • design prompt engineering workflows and retrieval-augmented generation (RAG) pipelines
  • implement monitoring, evaluation, and governance frameworks for AI/ML models in production
  • build and optimize robust data pipelines
  • design data architectures that support scale, real-time insights, and cross-functional AI workloads
  • implement ELT/ETL processes
  • operate in agentic client environments
  • rapidly prototype, iterate, and deploy solutions
  • translate business challenges into technical specifications
  • lead solution delivery from concept to production
  • agentic AI thinking
  • data engineering fluency
  • forward deployed presence