18 hours ago
Chennai, IndiaSenior
Responsibilities
- Design and build LLM application components including prompts, tools, agents, orchestration flows, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Create Microsoft Foundry agents with enterprise tools, knowledge, and memory, integrating Azure AI Search for RAG.
- Implement secure API and tool calls through API Management, Logic Apps, and Azure Functions.
- Build data ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Engineer safety and control capabilities including 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.
- Optimize AI infrastructure and hardware performance, power consumption, cost, scalability, and full-stack integration.
Requirements
- At least 5 years of experience.
- Bachelor’s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- Hands-on coding experience in Python 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 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 LLM application architecture patterns including RAG, function and tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics.
- Ability to implement traditional ML and generative AI components across ingestion, feature and 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 experience; Microsoft Fabric, Semantic Kernel, Prompt Flow, private networking, Defender for Cloud, and Responsible AI governance experience; and exposure to LangChain, LangGraph, LlamaIndex, 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 generative AI 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.