02 Aug
|
Recognized
|
Hyderabad
02 Aug
Recognized
Hyderabad
We are seeking a highly skilled and dedicated Senior GenAI Data Integration Engineer to lead the integration of a key
client’s advanced Generative AI capabilities (Gemini on Vertex AI) with
our core Business Intelligence platform, Looker. This is a critical,
dedicated role focused on transforming how our business users interact with
data, moving from static reports to dynamic, conversational, and explanatory
data narratives. The ideal candidate must be an expert in both LookML and LLM integration/prompt engineering, capable of building and
deploying production -ready, well -documented, and reusable conversational
analytics frameworks using exclusively Google tools and compliant languages.
Duration
This is a 6 -month
dedicated contract role, with the strong possibility of a longer -term extension
based on project success and future strategic needs.
Key Responsibilities
The successful candidate will be fully
responsible for the end -to -end development, deployment, and documentation of
three core capabilities, utilizing only Google Cloud Platform (GCP) services and standard compliant languages (Python/JavaScript):
1. Contextual Narrative Generation
â Design and implement a robust integration layer between Looker's
data models and the Gemini API (via Vertex AI).
â Develop and refine advanced Prompt Engineering strategies to analyze Looker
dashboard data, trends, and business drivers, ensuring contextual and accurate
narrative output.
â Automate the generation of high -quality, natural language
summaries and narratives explaining specific dashboard trends (e.g.
"Narrate the reason for the decrease in Total Approved Cost this
month").
2. Conversational Analytics and Dashboard Control
â Engineer and deploy a conversational chatbot interface using JavaScript/React and hosted on GCP (e.g. Cloud Run/App Engine).
â Utilize Python and the Looker API to effectively guide
the LLM in accurately determining the user's intent, translating the request
into the necessary Looker filter and view adjustments.
â Orchestrate a seamless workflow where a user's
conversational request triggers a dashboard change, followed by a data -driven
narrative.
3. Natural Language Querying (NLQ) for Data Access
â Build a scalable Natural Language Query (NLQ) layer that uses Gemini's
function calling or grounding features to translate complex user questions
into accurate, governed LookML or BigQuery SQL queries.
â Implement fine -tuned prompt templates that ground the
LLM's output using Looker's semantic layer to ensure query security and
data integrity within the BigQuery setting.
4. Framework Transparency, Documentation, and Handover
â Establish transparent, modular, and version -controlled code
repositories (e.g. Cloud Source Repositories or standard Git) for all
integration logic (Python/JavaScript).
â Create comprehensive, well -structured documentation for all components:
Prompt Engineering library, Looker API interface, and deployment configuration
on GCP services.
â Ensure the solution is built with modularity using Google -compliant
patterns to allow easy expansion and maintenance by a future team.
the Vertex AI platform. Expertise in Prompt Engineering for BI
tasks.
modeling and Looker API integration.
Vertex AI, Cloud Run/App Engine, and Cloud Functions.
in Python for API orchestration and backend logic. Strong skills in JavaScript for frontend integration. Experience with version control (Git).
principles, including Looker's Row -Level Security and Access Filters over BigQuery data.