16 hours 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 with grounding and RAG using BigQuery, Vertex AI Search, vector stores, Cloud Run, and GKE.
- Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Engineer safety, security, privacy, governance, authentication, authorization, lineage, audit logging, and other control components for production AI systems.
- Collaborate with architects, data engineers, product owners, and security stakeholders to deliver tested, observable, and maintainable software components.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- At least 5 years of software, data, or AI engineering experience, including cloud-native engineering and GenAI, LLM, NLP, or agentic AI delivery; an alternative path requires 7+ years with additional cloud and GenAI experience.
- Hands-on Python coding experience and strong understanding of APIs, distributed systems, testing, observability, and secure SDLC practices.
- Experience with Vertex AI, 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, and Cloud Logging and Monitoring.
- Understanding of LLM application architecture patterns including RAG, function and tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics.
- Experience delivering AI/ML or data products in at least one domain such as financial services, healthcare, retail, manufacturing, telecom, or media.
- Experience with Git-based development, automated testing, infrastructure-as-code, and agile delivery in client-facing environments.
- Preferred qualifications include Google Cloud professional certifications, BigQuery semantic layers, Dataform or dbt, LangChain or LlamaIndex, GKE ML workloads, MLOps on Vertex AI, LangGraph, Haystack, MLflow, FastAPI, Docker, Kubernetes, Responsible AI, model risk management, synthetic data, human-in-the-loop review, A/B testing, and GenAI cost optimization.
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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.