Accenture

Forward Deployed Technical Consultant -Databricks

Accenture$90K — $140K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree or relevant college program completion
  • 3 years experience with Databricks and large-scale big data projects
  • 1 year experience in designing and shipping agentic AI solutions
  • 1 year experience with AI platforms and Databricks AI tooling
  • 5 years programming in Python, Java, or equivalent
  • 2 years experience in MLOps and LLMOps including CI/CD and infrastructure as code

Responsibilities

  • Design enterprise-ready AI agents with specialized functions
  • Build RAG and multi-agent systems on Databricks and govern data/models
  • Develop foundational data on Lakehouse architectures for AI applications
  • Tailor agentic applications for diverse industries like finance and healthcare
  • Conduct workshops and proofs of concept with client stakeholders
  • Define metrics for agent performance including accuracy and cost
  • Process large distributed datasets using Databricks and Apache Spark

Benefits

  • Opportunity for career advancement in a cutting-edge field
  • Access to ongoing training and upskilling in AI and Databricks technology
  • Collaboration with multi-national teams across various industries
  • Involvement in pioneering projects that influence enterprise AI solutions
Full Job Description
You Are

A Forward Deployed Technical Consultant with strong experience building GenAI solutions and deep expertise in productionizing AI applications on the Databricks Intelligence Platform. You thrive in customer-facing complexity and deliver outcomes by applying the latest techniques from Mosaic AI Research, designing RAG and multi-agent systems with HuggingFace, LangChain, and DSPy, and shipping production-grade GenAI at scale on AWS, Azure, or GCP.

The Work

You will embed directly with clients as both technologist and trusted advisor. You will partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains.

Responsibilities
  • Design enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
  • Build RAG and multi-agent systems on Databricks using Mosaic AI, Vector Search, Model Serving, and MLflow; govern data and models through Unity Catalog.
  • Develop data and feature foundations on the Lakehouse (Delta Lake, DLT, Unity Catalog) that feed production AI applications.
  • Tailor and deploy agentic applications across industries such as finance, healthcare, and retail.
  • Conduct design workshops, proofs of concept, and code-with sessions with client stakeholders.
  • Define and use metrics to measure agent accuracy, latency, safety, and cost effectiveness.
  • Process large-scale distributed datasets on the Databricks Intelligence Platform with Apache Spark™.
  • Integrate LLM solutions with APIs, model monitoring, and prompt management.
  • Operate MLOps / LLMOps pipelines with CI/CD across the ML and LLM lifecycle.
  • This role requires in-office presence in the local ATC office 3 days per week.


Here's What You Need:
  • Bachelor's degree or completion of a college program in a related discipline
  • 3 years of experience on Databricks and large-scale big data projects (Delta Lake, DLT, Apache Spark, Unity Catalog). You've also built API layers that expose data and AI/ML capabilities to enterprise systems.
  • 1 year experience in designing and shipping agentic AI solutions in production.
  • 1 year experience in shipping agentic solutions in production (agents, orchestration, context engineering, RAG, workflows) using AI platforms (OpenAI, Claude, Vertex AI, open-source) and Databricks AI tooling (Mosaic AI, Vector Search, Model Serving, MLflow).
  • 5 years of experience in Python, Java, or equivalent. Comfortable with evaluation tooling, logging, monitoring, and observability.
  • 2 years' experience in shipping to production: CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging. Practical MLOps and LLMOps experience across the ML and LLM lifecycle (model training, serving, monitoring, prompt management).

English is required for this position as this role will be aligned to multi-national teams where English is the common language across our Global Enterprise. Due to the significant high volume of interactions with these English-speaking multi-national teams, which is inherent to this position, it is not possible to reorganize the company's activities to avoid this requirement.

Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location,role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation, based on full-timeemployment, for roles that may be hired as set forth below.

The recruiting efforts for this position are intended to fill a brand-new position. The base pay range shown below is intended as a guideline to reflect the majority of offers for this role.

It does not represent a maximum limit - in some cases, actual compensation may exceed the range where appropriate."

Information on benefits is here:

Role Location Annual Salary Range

British Columbia/Ontario $90,850 to $140,850

About Accenture

Accenture plc is a multinational professional services company that provides services in strategy, consulting, digital, technology, and operations. The company has more than 537,000 employees serving clients in more than 120 countries. Accenture operates across five business segments: Communications, Media & Technology; Financial Services; Health & Public Service; Products; and Resources. The company is headquartered in Dublin, Ireland, and has offices worldwide.
Learn more about Accenture
Size
624,000 employees
Market Cap
$173.8 billion
Industry
Net Income
$5.2 billion
Founded
1989
5 Year Trend
+11.2%
Revenue
$44.7 billion
NASDAQ

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