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 15 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 good 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.

Additional Information:

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).