Project Role : Large Language Model Architect

Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.

Must have skills : Large Language Models (LLMs)

Good to have skills : NA
Minimum 12 year(s) of experience is required

Educational Qualification : 15 years full time education

SUMMARY:
We are building out a world-class AI Delivery and Architecture team and are looking for exceptional engineers who want to shape how enterprise AI systems are designed, built, and operated at scale. This is a senior hands-on technical role that sits at the intersection of cloud engineering, LLM/GenAI application development, and AI infrastructure platform design.

You will drive architecture decisions, lead delivery across complex multi-cloud AI programs, and act as a force multiplier for the engineers around you. You will be equally at home writing production Python, reviewing infrastructure-as-code, and presenting architecture trade-offs to senior stakeholders.

Roles Responsibilities:

- Architect and deliver end-to-end GenAI solutions

- Define the AI roadmap for our key customers

- Design RAG pipelines, agentic workflows, and multi-model orchestration patterns that go beyond prototypes into production-grade systems.

- Lead technical design and governance

- Own Architecture Decision Records , run design reviews, establish engineering standards, and ensure solutions are reusable and maintainable across teams.

- Drive AI infrastructure platform engineering

- Build and scale model-serving infrastructure (Bedrock, Vertex AI, Azure OpenAI), and AI gateway/routing layers.




Strong system integration Skills

- Own multi-cloud delivery

- Architect and deliver across AWS, Azure, and GCP using IaC (Terraform/Pulumi), Kubernetes (EKS/AKS/GKE), and GitOps workflows.

- Embed observability and LLMOps from day one

- Instrument AI systems with OpenTelemetry, LLM-specific tooling , token/cost dashboards, and drift detection.

- Champion responsible AI and security

- Implement guardrails, output filtering, prompt injection defenses, data residency controls, and OWASP LLM Top 10 mitigations.

- Mentor and grow the team

- Guide engineers through complex technical challenges, lead code and architecture reviews, and help set the bar for engineering quality.

- Translate strategy into delivery

- Work closely with product, data science, and business stakeholders to turn AI objectives into executable technical roadmaps.

Professional Technical Skills:

- 12+ years of software engineering experience, with at least 2 years in AI/ML or GenAI delivery in production environments.

- Demonstrated track record of leading end-to-end delivery of complex, multi-cloud AI solutions not just prototypes.

- Prior experience as a tech lead or principal engineer who had managed 10+ engineers .

Core engineering

- Advanced Python packaging, production-grade code quality.

- Go or Java/Kotlin for platform and infrastructure services (advantageous).





- REST, gRPC, event-driven architecture (Kafka / Kinesis), clean architecture and domain-driven design.

LLM GenAI engineering

- Prompt engineering

- RAG pipeline design chunking strategies, embedding models, vector stores

- Knowledge graph design

- Agentic frameworks LangChain, LlamaIndex, AutoGen, or equivalent

- Fine-tuning experience LoRA, QLoRA, PEFT dataset curation and evaluation. vry positive to have

- LLM evaluation frameworks RAGAS, LLM-as-judge, regression testing, quality gates.

Cloud infrastructure

- Deep expertise in at least one of AWS, Azure, and GCP compute, networking, IAM, storage.

- Kubernetes (EKS, AKS, GKE)

- IaC Terraform (primary) Pulumi or AWS CDK also valued.

Architecture leadership

- Ability to produce and communicate architecture artefacts ADRs, solution design documents to technical and non-technical audiences.

- Well-Architected Framework knowledge across cloud providers.

- Responsible AI guardrails, PII redaction, output filtering, OWASP LLM Top 10, AI Act / GDPR awareness.

- Strong FinOps sensibility cost governance, token cost optimization, multi-cloud spend visibility.

- Cloud certifications AWS Solutions Architect Pro, Azure AI Engineer, Google Professional ML Engineer.

- Experience with AI ethics, model cards, bias auditing, or fairness testing.

- Background in platform engineering or building internal developer platforms (IDPs).

- Exposure to service mesh (Istio / Linkerd) and API gateway patterns for AI microservices.

- Experience working in regulated industries (finance, healthcare, public sector).

Qualification15 years full time education

📌 Large Language Model Architect (Karnataka)
🏢 Accenture
📍 Karnataka