Analytics & GenAI Architect – Life Sciences Technology

Guidehouse

Analytics & GenAI Architect designing and governing analytics solutions for life sciences technology. The role focuses on AI-enabled decision solutions for commercial and medical affairs stakeholders.

Posted 6/14/2026full-timeHyderabad • 🇮🇳 IndiaSeniorLeadWebsite

Tech Stack

Tools & technologies

CloudPythonReact

About the role

Key responsibilities & impact

Design and own semantic layer architecture and KPI marts supporting various life sciences commercial use cases (e.g., revenue performance, contracting analytics, engagement strategy, operational effectiveness, forecasting, etc.)
Translate business requirements into structured analytics models, dimensional schemas, and reusable metric definitions
Architect and implement GenAI solutions using RAG and agentic patterns, including embeddings, vector search, tool integrations, and multi-step workflows
Establish AI governance standards including evaluation frameworks, versioning practices, guardrails, prompt management, and output validation controls
Define monitoring, telemetry, and performance benchmarks for analytics pipelines and GenAI workloads
Ensure analytics products are scalable, performant, and aligned to enterprise data contracts and governance standards
Partner with Cloud & Data Platform Architect to optimize curated data layers for advanced analytics and AI grounding
Collaborate with Full Stack Architect to expose analytics and AI through secure, performant APIs and user experiences
Contribute reusable accelerators including KPI templates, semantic models, evaluation harnesses, and RAG reference architectures

Requirements

What you’ll need

7–10 years of experience in analytics engineering, BI architecture, data science
at least 3 years of experience in Gen AI solution delivery
Strong expertise in Python
Experience designing multi-agent topologies (supervisor–worker, router, debate)
Experience applying structured output and function-calling
Working knowledge of RAG architecture, vector search, embeddings, and reliable orchestration
Experience implementing advanced planning and reasoning patterns (React-style tool reasoning, task decomposition, dynamic routing)
Expertise in Agentic AI frameworks leveraging LangChain and LangGraph
Knowledge of MCP servers and tool integration
Experience implementing production-ready practices including testing, monitoring, CI/CD integration, and observability
Experience collaborating across distributed engineering teams
Bachelor’s degree in data science, computer science, engineering, or related field

Benefits

Comp & perks

comprehensive, total rewards package
competitive compensation
flexible benefits package
commitment to creating a diverse and supportive workplace

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

analytics engineeringBI architecturedata scienceGen AI solution deliveryPythonRAG architecturevector searchembeddingsCI/CD integrationobservability

Soft Skills

collaborationcommunicationproblem-solvingleadershiporganizational skills