17 hours ago
Hyderābād, IndiaSenior
Responsibilities
- Architect and build custom AI infrastructure and hardware solutions while optimizing performance, power consumption, cost, scalability, and vendor integration.
- Design and implement LLM application components including prompts, tools, agents, orchestration flows, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Build AWS-based agent workflows and secure RAG systems using Bedrock, Lambda, API Gateway, S3, OpenSearch, and Knowledge Bases.
- Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured enterprise content.
- Engineer AI safety and control components including PII detection and redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage, and audit logging.
- Translate solution designs into tested, observable, maintainable software components and contribute code, design decisions, reusable patterns, and technical documentation.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering artifacts.
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 domains 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, including RAG, function and tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics.
- Ability to implement traditional ML and GenAI components across ingestion, 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 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 exposure to LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker, Kubernetes, responsible AI, model risk management, synthetic data generation, 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.