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 LLM applications and agents with grounding, RAG, tool calling, event-driven workflows, model evaluation, and deployment on Cloud Run or GKE.
- Implement ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Engineer safety and control capabilities such as 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.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
Requirements
- At least 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, along with 15 years of full-time education.
- Hands-on Python coding experience and strong understanding of APIs, distributed systems, testing, observability, secure SDLC practices, CI/CD, and automated testing.
- Experience delivering AI/ML or data products in at least one 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.
- 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.
- Practical knowledge of security, privacy, governance, performance, scalability, reliability, and cost controls for production AI systems.
- Experience with Git-based development, 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; Vertex AI MLOps; 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.
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.