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.

Responsibilities :

- Architect and deliver end-to-end GenAI solutions and define the AI roadmap for key customers.

- Design RAG pipelines, agentic workflows, and multi-model orchestration patterns for production-grade systems.

- Lead technical design and governance, own Architecture Decision Records, and establish engineering standards.

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

- Own multi-cloud delivery across AWS, Azure, and GCP using IaC (Terraform/Pulumi) and Kubernetes.

- Embed observability and LLMOps using OpenTelemetry and drift detection.

- Champion responsible AI, security, and OWASP LLM Top 10 mitigations.

- Mentor and grow the team, guiding engineers through complex technical challenges.

Technical Skills :

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

- Prior experience as a tech lead or principal engineer managing 10+ engineers.

- Advanced Python (packaging, production-grade code quality).

- Experience with Go or Java/Kotlin for platform services.

- Expertise in RAG pipeline design, agentic frameworks (LangChain, LlamaIndex, AutoGen), and LLM evaluation frameworks (RAGAS, LLM-as-judge).

- Deep expertise in at least one cloud provider (AWS/Azure/GCP), Kubernetes, and Terraform.

- Strong FinOps sensibility for token cost optimization and multi-cloud spend visibility.

Preferred Qualifications :

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

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

- Knowledge of AI ethics, model cards, and bias auditing.