3 days 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.
- Create Microsoft Foundry agents and integrate enterprise tools, knowledge, memory, Azure AI Search, API Management, Logic Apps, and Azure Functions.
- Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured content.
- Engineer AI safety and control components 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.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- Minimum five years of experience is required; the qualifications also describe typical profiles with seven or more years of software, data, or AI engineering experience.
- At least two years of cloud-native engineering and one year of GenAI, LLM, NLP, or agentic AI delivery experience, with additional typical-experience criteria of three years in cloud-native architecture or engineering and one to two years in GenAI, LLM, NLP, or agentic AI delivery.
- Hands-on Python coding experience and strong knowledge of APIs, distributed systems, testing, observability, secure SDLC practices, Git-based development, CI/CD, infrastructure-as-code, and agile delivery.
- Experience delivering AI/ML or data products in domains such as banking, insurance, healthcare, public sector, retail, or energy.
- Experience with 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 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.
- 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.