3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Design and build LLM application components such as prompts, tools, agents, orchestration flows, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Create Azure Foundry-based agents and integrate enterprise tools, knowledge, memory, Azure AI Search, API Management, Logic Apps, Functions, tracing, evaluations, guardrails, and policy controls.
- 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, authentication, authorization, lineage, and audit logging.
- Collaborate with architects, data engineers, product owners, and security stakeholders to turn solution designs into tested, observable, and maintainable software.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
- Serve as a hands-on technical or engineering lead, contributing code, design decisions, documentation, and reusable patterns across client engagements.
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 professional experience.
- 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 domains such as banking, insurance, healthcare, public sector, retail, or energy.
- Hands-on experience with Microsoft Foundry or Azure AI Foundry, Foundry Agent Service, Azure OpenAI models, Azure AI Search, Prompt Flow, Azure Functions, Logic Apps, API Management, Event Grid, Microsoft Fabric, Entra ID, Key Vault, Azure Monitor, and Application Insights.
- 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 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 Azure AI Engineer, Azure Solutions Architect, or Azure Developer certification; Microsoft Fabric, Semantic Kernel, Prompt Flow, private networking, Defender for Cloud, Responsible AI governance, LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker, Kubernetes, model risk management, synthetic data, human-in-the-loop review, A/B testing, and GenAI cost optimization experience.
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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.