3 days ago
Coimbatore, IndiaSenior
Responsibilities
- Design and build LLM application components, including prompts, tools, agents, orchestration flows, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Develop Cortex-powered LLM applications and agents within Snowflake, including RAG, document intelligence, Streamlit and Snowpark applications, RBAC, masking, lineage, and monitoring.
- Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured content.
- Engineer safety and control features such as 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, and maintainable software components.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
Requirements
- A bachelor’s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline is required.
- At least 5 years of software, data, or AI engineering experience is required, with cloud-native engineering and GenAI, LLM, NLP, or agentic AI delivery experience; the posting also lists an alternative profile of 7+ years with additional architecture and GenAI experience.
- Hands-on Python coding experience and strong understanding of APIs, distributed systems, testing, observability, secure SDLC practices, Git-based development, CI/CD, and infrastructure-as-code are required.
- Experience with Snowflake Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Cortex AI Functions or LLM Functions, Snowpark, Streamlit in Snowflake, Dynamic Tables, Tasks, Streams, Snowflake ML, RBAC, masking policies, and observability is required.
- Understanding of LLM application architecture, RAG, function and tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics is required.
- Experience implementing traditional ML and GenAI components across ingestion, data preparation, model integration, deployment, monitoring, and continuous improvement is required.
- Experience delivering AI/ML or data products in an industry such as banking, insurance, retail, healthcare, travel, logistics, or telecom is required.
- Preferred qualifications include SnowPro Advanced Architect or Data Engineer exposure, Snowpark Python, Streamlit, semantic models, dbt, Native Apps, Snowflake governance, data sharing, cost and performance tuning, and frameworks such as 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.