Forward Deployed AI Engineer

About the role

We are seeking a Senior Engineer who will play a pivotal role in building end-to-end AI solutions. This individual will be responsible for designing and implementing the data foundation, building full-stack applications, and orchestrating pipelines and automation.

Responsibilities

• Own work end to end — from discovery and solution shaping through system design, build, and production deployment.
• Design and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production.
• Build full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usable.
• Use AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or quality.
• Design and build AI capabilities where they fit — RAG pipelines, agentic workflows, and LLM-in-the-loop processing — and compose them via MCP servers, Skills, and Plugins.
• Orchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the job.
• Stand up and own CI/CD and cloud deployments on AWS and Azure.
• Translate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiences.
• Contribute reusable accelerators and technical assets back to the Data Practice.

Requirements

• Data Engineering Foundation: Data modeling and schema design, hands-on data pipeline experience, Slowly Changing Dimensions (SCD), dbt Experience, Advanced SQL, Modern data platform in depth, Data quality thinking, System design, AI-paired engineering, CI/CD and cloud deployment ownership.
• Full-Stack AI Product Development: Python as a primary language, FastAPI, Next.js, PostgreSQL, System design, AI-paired engineering, CI/CD and cloud deployment ownership.
• Ways of Working: Comfortable in client-facing delivery, Customer-first mindset, End-to-end ownership instinct.

Qualifications

• Must have genuine production depth across data engineering and full-stack development.

Skills

• Commercial data fluency: Experience evaluating how commercial data flows across CRM and ERP.
• Agentic AI depth: LangGraph or comparable: multi-agent coordination, tool use, memory, and state management.
• RAG engineering: retrieval strategies, vector stores, chunking, re-ranking, and evaluation.
• Experience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role.
• Workflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows).
• Streaming data patterns: Kafka, Spark Streaming, or Flink.
• Vector databases: Pinecone, Weaviate, Qdrant, or pgvector.
• Experiment tracking: MLflow, Weights & Biases, or similar.
• Contributions to open-source AI or data tooling, or to internal accelerators and frameworks.
• Multi-cloud or hybrid cloud architecture exposure.

Benefits

• High-autonomy, high-ownership work across the full arc of real client problems.
• A team that takes AI tooling seriously and expects you to use it, not just name-drop it.
• Access to the full modern data and AI stack — no one-tool shops.
• Room to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.

Pay

• Competitive salary based on experience and performance.

Schedule

• Remote (Global)