20 hours ago
Pune, IndiaSenior
Responsibilities
- Design and build LLM application components including prompts, tools, agents, orchestration flows, memory and context handling, retrieval pipelines, and evaluation harnesses.
- Design AWS-based agent workflows using Bedrock, serverless services, enterprise APIs, RAG, OpenSearch, S3, and Knowledge Bases.
- Implement ingestion, parsing, chunking, enrichment, embeddings, vector search, and retrieval workflows for structured and unstructured content.
- Build 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, and maintainable software components.
- Maintain component designs, integration specifications, deployment runbooks, evaluation results, and reusable engineering patterns.
- Provide hands-on technical leadership at Level 8 or Level 9 while contributing code and design decisions.
Requirements
- At least 7.5 years of professional experience is required.
- 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 knowledge of APIs, distributed systems, testing, observability, and secure SDLC practices.
- Experience with cloud-native engineering or architecture and delivery of GenAI, LLM, NLP, agentic AI, AI/ML, or data products.
- Hands-on experience with Amazon Bedrock, Bedrock Agents or AgentCore, Knowledge Bases, Guardrails, Lambda, API Gateway, Step Functions, OpenSearch, SageMaker, IAM, CloudWatch, CloudTrail, VPC, KMS, and S3.
- Knowledge of RAG, function and tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases, and evaluation metrics.
- Experience implementing AI/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.
- Experience delivering AI/ML or data products in an industry such as banking, insurance, healthcare, retail, telecom, or capital markets.
- 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, MLflow, FastAPI, Docker, Kubernetes, 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.