15 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 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 to deliver tested, observable, and maintainable software components.
- Create component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
- Provide hands-on technical or engineering leadership and contribute code and design decisions across client engagements.
Requirements
- Minimum five years of professional experience.
- Bachelor's degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline, or 15 years of full-time education.
- Hands-on Python coding experience and strong understanding of APIs, distributed systems, CI/CD, testing, observability, and secure SDLC practices.
- Experience delivering AI/ML or data products in at least one domain such as financial services, healthcare, retail, manufacturing, telecommunications, 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.
- Understanding of RAG, function and tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics.
- Ability to implement traditional ML and GenAI components across ingestion, data preparation, model integration, deployment, monitoring, and continuous improvement.
- Knowledge of security, privacy, governance, performance, scalability, reliability, and cost controls for production AI systems.
- Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code, and agile delivery in client-facing environments.
- 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; MLOps on Vertex AI; LangGraph; Haystack; MLflow; FastAPI; Docker; Kubernetes; 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.