18 hours ago
Hyderābād, IndiaSenior
Responsibilities
- Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines, and evaluation harnesses.
- Build data-grounded agentic applications on Databricks using RAG, Delta tables, Vector Search, governed features, MLflow, Model Serving, and Unity Catalog.
- Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for 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 turn solution designs into tested, observable, and maintainable software.
- 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, including 2 years of cloud-native engineering and 1 year of GenAI, LLM, NLP, or agentic AI delivery; the posting also lists a typical 7+ year experience track with 3 years of cloud-native experience.
- Hands-on Python coding experience and strong knowledge of APIs, distributed systems, testing, observability, and secure SDLC practices.
- Experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow tracing and evaluation, Vector Search, Unity Catalog, Delta Lake, Lakehouse Monitoring, Feature Store, Databricks Workflows, Jobs, notebooks, and Model Training.
- Knowledge of LLM application architecture, including 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 ingestion, data preparation, model integration, deployment, monitoring, and continuous improvement.
- Experience with 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 financial services, healthcare, manufacturing, retail, telecom, or life sciences.
- Preferred qualifications include Databricks certifications, Spark/PySpark, Delta Live Tables, LangGraph, LangChain, model fine-tuning, lakehouse optimization, LlamaIndex, Haystack, 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.