15 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Design and build LLM application components such as prompts, tools, agents, orchestration flows, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Develop AWS-based agent workflows using Bedrock, Lambda, API Gateway, Step Functions, S3, OpenSearch, and Knowledge Bases.
- Implement ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Engineer AI 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 deliver tested, observable, maintainable software components.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
- Contribute code and technical leadership as a Level 9 technical lead or Level 8 engineering lead across active client engagements.
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 cloud-native engineering and GenAI, LLM, NLP, or agentic AI delivery; an alternative path requires 7+ years with specified cloud architecture and GenAI experience.
- Hands-on Python coding experience and strong understanding of APIs, distributed systems, testing, observability, and secure software development practices.
- Experience with Amazon Bedrock, Bedrock Agents or AgentCore, Knowledge Bases, Guardrails, Lambda, API Gateway, Step Functions, OpenSearch Serverless or Vector Engine, SageMaker, IAM, CloudWatch, CloudTrail, VPC, KMS, and S3.
- Understanding 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 data preparation, model integration, deployment, monitoring, and continuous improvement.
- Experience delivering AI, ML, or data products in an industry such as banking, insurance, healthcare, retail, telecom, or capital markets.
- Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code, and agile client delivery.
- Preferred qualifications include AWS Solutions Architect or Machine Learning Specialty certification, CDK or Terraform, EKS, Bedrock model evaluation, Amazon Q, responsible AI controls, FinOps, open-source AI frameworks, Docker, Kubernetes, model risk management, synthetic data, human-in-the-loop review, A/B testing, and GenAI cost optimization.
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.