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.
- Develop agent workflows using Amazon Bedrock and serverless AWS patterns, integrating enterprise APIs through Lambda and API Gateway.
- Build secure RAG solutions using S3, OpenSearch, and Knowledge Bases, and tune prompts and evaluation suites for accuracy, relevance, faithfulness, and safety.
- Implement ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Engineer 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.
- At least 5 years of software, data, or AI engineering experience, including cloud-native engineering and GenAI, LLM, NLP, or agentic AI delivery experience.
- Hands-on Python coding experience and strong understanding of APIs, distributed systems, testing, observability, and secure software development practices.
- Experience delivering AI/ML or data products in an industry such as banking, insurance, healthcare, retail, telecom, or capital markets.
- Hands-on 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, 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.
- Practical 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 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, and FinOps for GenAI workloads.
- Preferred experience 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.
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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.