3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Design and build LLM application components, including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines, and evaluation harnesses.
- Develop Cortex-powered LLM applications and agents within Snowflake, including RAG, document intelligence, Streamlit/Snowpark applications, RBAC, masking, lineage, and monitoring.
- Implement ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Engineer safety and control capabilities 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 turn solution designs into tested, observable, maintainable software.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
Requirements
- At least 7.5 years of professional experience.
- Bachelor’s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline, with 15 years of full-time education.
- Hands-on Python coding experience and strong understanding of APIs, distributed systems, CI/CD, testing, observability, and secure SDLC practices.
- Experience delivering AI/ML or data products in an industry domain such as banking, insurance, retail, healthcare, travel, logistics, or telecom.
- Hands-on experience with Snowflake Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Cortex AI Functions/LLM Functions, Snowpark, Streamlit in Snowflake, Dynamic Tables, Tasks, Streams, Snowflake ML, RBAC, masking policies, and observability.
- Understanding of LLM application architecture, including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics.
- Ability to implement 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 SnowPro Advanced Architect/Data Engineer certification or Snowflake ML exposure, Snowpark Python, Streamlit, semantic models, dbt, Native Apps, Snowflake governance, data sharing, and cost/performance tuning.
- 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.