3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Design and build LLM application components including prompts, tools, agents, orchestration flows, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Build Gemini-powered applications and agents using grounding/RAG, BigQuery, Vertex AI Search, vector stores, tool calling, and event-driven workflows.
- Implement ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Deploy AI services on Cloud Run or GKE and build observable, maintainable, production-quality software components.
- Engineer PII detection and redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage, and audit logging.
- Collaborate with architects, data engineers, product owners, and security stakeholders and maintain designs, integration specifications, runbooks, evaluation results, and reusable engineering patterns.
Requirements
- At least 7.5 years of experience is required.
- Bachelor’s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline is required.
- Hands-on coding experience in Python and strong knowledge of APIs, distributed systems, CI/CD, testing, observability, and secure SDLC practices.
- Experience delivering AI/ML or data products in at least one industry domain such as financial services, healthcare, retail, manufacturing, telecom, or media.
- Experience with Vertex AI, Gemini Enterprise Agent Platform, Gemini models, Model Garden, Agent Builder or Agent Engine patterns, Vertex AI Search, BigQuery, AlloyDB or Cloud SQL vector support, Cloud Run, GKE, Pub/Sub, Cloud Functions, IAM, Cloud Logging, and Cloud Monitoring.
- Knowledge of RAG, function and tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics.
- Experience with data ingestion, preparation, model integration, deployment, monitoring, continuous improvement, infrastructure-as-code, automated testing, and Git-based development.
- Preferred qualifications include Google Cloud Professional Data Engineer, Professional ML Engineer, or Cloud Architect certification; BigQuery semantic layers; Dataform or dbt; LangChain or LlamaIndex; GKE ML workloads; Vertex AI MLOps; and frameworks such as LangGraph, Haystack, MLflow, FastAPI, Docker, and Kubernetes.
- Preferred experience includes Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing, and GenAI cost optimization.
Tech Stack
Categories
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.