GCP Data/RAG Engineer - Senior Consultant

Summary
Position Summary
#S&S2027
Customer
The Customer Team empowers organizations to build deeper relationships with customers through innovative strategies, advanced analytics, GenAI, transformative technologies, and creative design. We enable Deloitte client service teams to enhance customer experiences and drive sustained growth and customer value creation and capture, through customer and commercial strategies, digital product and innovation, marketing, commerce, sales, and service.
Position Summary
Level: Senior Consultant, Data and Insights
As a Senior Consultant at Deloitte Consulting, you will design, develop, and deploy enterprise-scale software solutions, lead the creation of robust pipelines and manage code deployment across environments. You will collaborate with cross-functional, global teams to translate functional requirements into effective deliverables, independently guiding and mentoring junior team members. Your role spans the full project lifecycle, including estimation, planning, execution, and tracking key metrics for analysis, ensuring high-quality and timely delivery of solutions.
Work you’ll do:
Lead scrum teams for Epics/Features; provide technical design and develop stories per sprint.
Lead estimation for Epics/Features; support project plan/timeline, upsell/cross-sell via estimations.
End-to-end ownership of functional modules; lead story grooming/solutioning with onshore/client teams.
Ensure quality with unit/peer review; conduct RCA on defects and provide timely fixes.
Lead code reviews; ensure team adherence to standards; guide on customization vs configuration.
Lead demos to clients; prepare status/QRM reports; present risks/issues in project meetings.
Prepare process flows, release notes, pipeline management; leverage Delivery Excellence/automation assets.
Prepare Gantt charts, RACII/dependency matrix; maintain project leverage with Manager.
Prepare engagement review documentation; support upsell/cross-sell in account/project.
Architect, lead, and optimize Agentforce/GenAI integrations; drive adoption of GenAI/Agentic solutions.
Lead technical solutioning, code reviews, and customization/configuration decisions for Agentic solutions.
Drive innovation in GenAI/Agentforce use cases; ensure best practices in AI/ML integration with Salesforce.
Qualifications
Must Have Skills/Project Experience/Certifications:
7+ years of hands-on experience in data engineering and/or AI/ML, with at least 3+ years of deep, practical experience on Google Cloud Platform (GCP)
Strong expertise in building and operationalizing Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embedding generation, vector indexing, and retrieval optimization
Hands-on experience with GCP data services: BigQuery, Dataflow, Dataproc, Cloud Composer (Airflow), Pub/Sub, Cloud Storage, and Dataplex
Proficiency with Vertex AI Search, Vertex AI RAG Engine, and Gemini APIs for building production-grade RAG and GenAI solutions
Experience with vector databases and semantic search technologies including Vertex AI Vector Search, Pinecone, Weaviate, or pgvector
Strong programming skills in Python (including LangChain, LlamaIndex, or similar RAG frameworks) and SQL for data pipeline and model development
Experience designing and implementing data pipelines for structured and unstructured data sources (PDFs, documents, web content, enterprise databases) to feed RAG systems
Familiarity with embedding models (text-embedding-gecko, OpenAI Ada, or open-source alternatives) and re-ranking strategies for retrieval quality improvement
Ability to evaluate and improve RAG system performance using metrics such as faithfulness, answer relevancy, context precision, and RAGAS benchmarks
Collaborate with AI/ML engineers, architects, and client teams to deliver end-to-end GenAI data solutions from requirements to production
Contribute to pre-sales, client proposals, and practice development activities related to GCP data and AI offerings
Good to Have Skills/Project Experience/Certifications:
Experience with knowledge graph integration (Neo4j, Spanner Graph) for Graph RAG or hybrid retrieval architectures
Familiarity with multi-modal RAG pipelines incorporating image, audio, or video content via Gemini multimodal models
Experience with MLOps practices on GCP including Vertex AI Pipelines, Model Registry, and Feature Store
Knowledge of data governance, lineage, and cataloging tools such as Dataplex, Data Catalog, or Collibra on GCP
GCP Professional Data Engineer or Professional Machine Learning Engineer certification preferred
Experience with streaming data ingestion and real-time RAG patterns using Pub/Sub and Dataflow
Location: Bengaluru/Hyderabad/Pune/Chennai