19 hours ago
Pune, 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 AI Foundry agents with enterprise tools, knowledge, and memory, integrating Azure AI Search, API Management, Logic Apps, Functions, tracing, evaluations, guardrails, and policy controls.
- Implement 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 convert solution designs into tested and maintainable software components.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
- Act as a hands-on technical or engineering lead by contributing code, design decisions, and production-quality implementations.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- Minimum 7.5 years of experience is required.
- Experience delivering software, data, or AI engineering solutions, including cloud-native engineering and GenAI, LLM, NLP, or agentic AI delivery.
- Hands-on Python coding experience and strong knowledge of APIs, distributed systems, testing, observability, and secure software development practices.
- Experience with Microsoft 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 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.
- Experience delivering AI/ML or data products in an industry such as banking, insurance, healthcare, public sector, retail, or energy.
- Preferred experience includes Azure AI Engineer, Azure Solutions Architect, or Azure Developer certifications; Microsoft Fabric, Semantic Kernel, Prompt Flow, private networking, Defender for Cloud, and Responsible AI governance.
- Preferred exposure includes LangChain, LangGraph, LlamaIndex, 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.