24 days ago
Gurgaon, IndiaSenior
Responsibilities
- Design and build LLM application components, including prompts, tools, agents, orchestration, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Create Microsoft Foundry agents integrating enterprise tools, knowledge, memory, Azure AI Search, API Management, Logic Apps, and Azure Functions.
- Implement 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 convert designs into tested, observable, 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, with Level 8/9 expectations ranging from 5+ to 7+ years.
- At least 2–3 years of cloud-native engineering or architecture experience, depending on level.
- At least 1–2 years of GenAI, LLM, NLP, or agentic AI delivery experience, depending on level.
- Hands-on Python coding experience and strong knowledge of APIs, distributed systems, testing, observability, and secure software development practices.
- Experience delivering AI/ML or data products in an industry domain such as banking, insurance, healthcare, public sector, retail, or energy.
- Experience with 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.
- Experience implementing 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, 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 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.