1 day ago
Bengaluru, IndiaStaff+
Responsibilities
- Architect and deliver production-grade end-to-end GenAI solutions for enterprise customers.
- Design RAG pipelines, agentic workflows, multi-model orchestration, knowledge graphs, and LLM evaluation systems.
- Own architecture decision records, design reviews, engineering standards, and solution governance.
- Build and scale model-serving infrastructure and AI gateway or routing layers across multiple clouds.
- Deliver cloud infrastructure using infrastructure as code, Kubernetes, and GitOps workflows.
- Implement observability, LLMOps, token and cost dashboards, drift detection, and quality gates.
- Implement responsible AI and security controls including guardrails, output filtering, prompt-injection defenses, data-residency controls, and OWASP LLM Top 10 mitigations.
- Lead technical delivery, mentor engineers, conduct code and architecture reviews, and establish engineering quality standards.
- Partner with product, data science, business, and customer stakeholders to turn AI objectives into executable roadmaps.
- Contribute to prompt engineering, fine-tuning, dataset curation, model evaluation, FinOps, and multi-cloud cost governance.
Requirements
- At least 7.5 years of experience is required; the technical qualifications specify 12+ years of software engineering experience.
- At least 2 years of production experience in AI/ML or GenAI delivery.
- A record of leading end-to-end delivery of complex, multi-cloud AI solutions beyond prototypes.
- Prior experience as a technical lead or principal engineer managing 10 or more engineers.
- Advanced Python skills for packaging and production-grade development.
- Experience with Go or Java/Kotlin for platform and infrastructure services is advantageous.
- Knowledge of REST, gRPC, event-driven architecture, Kafka or Kinesis, clean architecture, and domain-driven design.
- Experience designing RAG pipelines, embedding models, vector stores, knowledge graphs, agentic frameworks, and LLM evaluation systems.
- Fine-tuning experience with LoRA, QLoRA, and PEFT is very good to have.
- Deep expertise in at least one of AWS, Azure, or GCP, including compute, networking, IAM, and storage.
- Experience with Kubernetes, Terraform, Pulumi or AWS CDK, and multi-cloud infrastructure delivery.
- Ability to communicate architecture artifacts to technical and non-technical audiences.
- Knowledge of cloud Well-Architected Frameworks, responsible AI, FinOps, AI ethics, model cards, bias auditing, or fairness testing.
- Experience with platform engineering, internal developer platforms, service mesh, API gateway patterns, or regulated industries is advantageous.
- Cloud certifications such as AWS Solutions Architect Professional, Azure AI Engineer, or Google Professional Machine Learning Engineer are advantageous.
- 15 years of full-time education is required.
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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.