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Founding Platform Engineer

Founding Platform Engineer

Full Time

Chennai

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About Yavar:

  • Yavar is an enterprise AI company delivering service-as-a-software with pre-built agentic platforms. We help organizations reinvent workflows, automate complex use cases, and achieve clarity with data—while ensuring privacy, security, and verifiability. We’re looking for an AI Engineer to build and scale next-gen GenAI products that blend large language models, real-time inference, and intelligent automation.

  • At the core of this ecosystem is an enterprise platform for responsible agentic intelligence, where every workflow is governed, every agent is accountable, and every operation runs on the infrastructure enterprises already have.This platform serves as the foundation of Yavar’s enterprise AI ecosystem, enabling the rapid development and deployment of multiple AI-driven applications across domains such as sustainability intelligence, financial operations, customer intelligence, and enterprise workflow automation.

  • To build this platform foundation, Yavar is looking for a Founding Platform Engineer who will architect and build the core platform infrastructure. This role will play a critical part in shaping the technical backbone of Yavar’s AI ecosystem.

Role Overview:

  • The Founding Platform Engineer will be responsible for designing and building the core platform architecture that powers Yavar’s enterprise AI applications.

  • The role involves building the shared infrastructure, platform services, AI orchestration layers, and data frameworks that enable multiple agentic AI applications to run on a unified enterprise platform.

  • This is a hands-on platform engineering role that requires deep expertise in distributed systems, cloud-native architecture, platform infrastructure, and modern AI application frameworks.

  • The individual will work closely with engineering leadership, AI teams, and product teams to ensure that the platform architecture enables rapid innovation while maintaining scalability, governance, and enterprise-grade reliability.

Key Responsibilities:

  • Platform Architecture and Infrastructure:

    • Design and build the core platform foundation, including:

    • Multi-tenant platform architecture

    • Shared platform services and infrastructure

    • API gateway and integration frameworks

    • Workflow orchestration frameworks

    • Data ingestion and processing pipelines

    • Platform extensibility for multiple AI applications

    • Ensure the platform supports multiple enterprise AI products built on a shared core infrastructure.

  • AI Platform Infrastructure:

    • Design and implement infrastructure supporting:

    • Large Language Model (LLM) orchestration

    • Retrieval Augmented Generation (RAG) architectures

    • AI inference pipelines

    • Model lifecycle management

    • Vector databases and semantic search infrastructure

    • Agent orchestration frameworks

    • Ensure the platform supports production-scale AI workloads.

  • Enterprise Platform Capabilities:

    • Architect enterprise-grade platform capabilities including:

    • Multi-tenancy and tenant isolation frameworks

    • Identity management and authentication layers

    • Role-based access control (RBAC)

    • Enterprise integrations and system connectors

    • Data governance and compliance mechanisms

    • Observability, monitoring, and auditability

  • Platform Scalability and Reliability:

    • Design and build systems capable of supporting:

    • High-concurrency enterprise workloads

    • Large-scale document ingestion and data pipelines

    • Real-time and batch data processing frameworks

    • Horizontal scaling of platform services

    • High availability and reliability

  • Platform Standards and Engineering Practices:

    • Define and implement best practices for:

    • Microservices architecture

    • API-first platform design

    • DevOps and CI/CD pipelines

    • Cloud-native deployment models

    • Platform security and reliability engineering

    • Observability and monitoring frameworks

  • Cross-Team Collaboration:

    • Work closely with:

    • Head of Engineering

    • Innovation and AI teams

    • Product leadership

    • Guide engineering teams in building applications that leverage platform capabilities and shared services.

Experience and Qualifications :

  • Professional Experience:

    • 10 – 12 years of experience in software engineering and platform development

    • Experience building large-scale SaaS platforms or platform infrastructure

    • Strong experience designing distributed systems and cloud-native architectures

    • Experience building multi-tenant enterprise platforms

    • Experience handling high-concurrency and low-latency systems at scale

    • Experience implementing tenant isolation strategies (DB/schema/row-level)

    • Experience building self-serve developer workflows and platform tooling (CI/CD, infra provisioning)

    • Candidates should have experience building platform infrastructure supporting multiple applications or services.

  • Technical Expertise:

    • Strong expertise in:

    • Cloud platforms (GCP / Azure / AWS)

    • Kubernetes and container orchestration

    • Microservices architecture

    • Event-driven systems

    • API-first architectures

    • Data processing and pipeline frameworks

    • Experience with observability tools

    • Experience with system reliability patterns

  • AI Infrastructure Experience (Preferred) :

    • Experience working with:

    • LLM-based applications

    • RAG architectures

    • AI pipelines and orchestration frameworks

    • Vector databases

    • Agent-based AI systems

  • Engineering Background:

    • Strong hands-on engineering experience with languages such as:

    • Python, Go

    • Experience working with data pipelines and streaming systems (Kafka, Spark, Flink, Airflow)

    • Experience integrating with data warehouses (BigQuery, Snowflake, Redshift)

    • Candidates should demonstrate a builder mindset with the ability to translate architecture into working systems.

Ideal Candidate Profile:

  • The ideal candidate has previously:

  • Built the platform infrastructure of a SaaS product

  • Designed systems supporting multiple applications on a shared platform

  • Built distributed systems handling large-scale enterprise data workloads

  • Worked in product companies or high-scale technology environments

  • Experience working in platform engineering teams or product infrastructure teams will be strongly preferred.

What Success Looks Like:

  • Within the first 3 months, the candidate will:

  • Establish the core platform architecture

  • Build a reusable enterprise AI platform foundation

  • Enable rapid development of multiple agentic AI applications

  • Implement scalable AI infrastructure and enterprise data pipelines

How to apply:

  • Send your resume to digital@yavar.ai

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