Senior AI Platform Engineer

For over four decades PAR Technology Corporation (NYSE: PAR) has been a leader in restaurant technology empowering brands worldwide to create lasting connections with their guests. Our innovative solutions and commitment to excellence provide comprehensive software and hardware that enable seamless experiences and drive growth for over 100000 restaurants in more than 110 countries. Embracing our Better Together ethos we offer Unified Customer Experience solutions combining point-of-sale digital ordering loyalty and back-office software solutions as well as industry-leading hardware and drive-thru offerings. To learn more visit or connect with us on LinkedIn X (formerly Twitter) Facebook and Instagram.

Position Description:

PAR is looking for a Senior AI Platform Engineer to be the technical cornerstone of our newly formed AI Platform team. You will design build and operate a production-grade multi-tenant AI Platform on AWS using LLM orchestration services tools and libraries enabling engineering teams in PAR Restaurant group to build deploy and scale AI agents and agentic workflows that surface directly to customers.

The ideal candidate is deeply technical with 78 years of hands-on ML/AI engineering experience especially with building AI platform components and equally effective as a communicator team player and a collaborator. You write production code define architectural standards and can explain a policy-as-code framework a RAG pipeline and a team sprint plan with equal clarity.

This is an ideal role for someone who is hands-on obsessed with performance and scalability and deeply experienced with productionizing multi-agent systems from graph-based orchestration and real-time streaming to RAG pipelines and multi-model routing at production scale.

Position Location: Gurugram / Jaipur India (Hybrid)

Reports To: Director of AI and Analytics

What Were Looking For:

AI Platform Engineering Core Orchestration & Runtime

Build the PAR Restaurant AI Platform - a dual-purpose system that at build time gives product engineering teams the APIs SDKs tool registry and knowledgebase services to author and deploy agents and at runtime hosts those agents in production across all PAR product lines with multi-tenant isolation security guardrails content safety governance and compliance controls

Design and build production-grade GenAI microservices (e.g. FastAPI) and stateful multi-agent workflows including selection of the appropriate orchestration pattern (model-driven graph-based etc.) based on auditability latency and complexity requirements of each agent class

Design and operate an automated content ingestion pipeline that includes content upload chunking embedding model selection vector storage and OpenSearch Serverless indexing - exposing knowledgebase retrieval as a fully managed self-service capability for all product teams

AI Platform Engineering Tooling & Developer Experience

Build and maintain the platform APIs and developer tooling including MCP-compatible tool interfaces real-time streaming APIs (SSE / WebSocket) and agent configuration SDKs that allow engineers to create deploy run and observe multi-agent workflows with configurable guardrails and content safety controls per agent class

Own internal AI SDK ergonomics for both audiences: agent-authoring engineers and application-backend engineers consuming the streaming invocation API

Build and maintain the Platform Onboarding Copilot (Slack-integrated); produce clear actionable technical documentation and architectural decision records (ADRs); present platform strategy to non-technical stakeholders

Own the platform observability stack as a product not an afterthought. Provides platform tools for engineers to fetch observability metrics and distributed traces auto-provisioned on every agent deployment; an LLM eval suite (e.g. LangSmith Ragas) that gates CI/CD on quality regressions; and per-tenant cost attribution with spend alerting giving every engineering team full visibility into their inference consumption

Define quality bars and eval suites per agent class; no agent reaches production without a documented passing score threshold thereby establish evaluation as a first-class engineering discipline across the team

Governance Compliance Multi-Tenancy

Design multi-tenant isolation from day one including namespace isolation tenantid injection and policy-as-code enforcement ensuring there is no cross-brand data or context pollution.

Own the policy-as-code library for PAR-specific policy sets covering PAR Restaurant product lines while ensuring platform compliance with SOC 2 Type II PCI DSS GDPR and CCPA; author and maintain the GDPR deletion pipeline

Define and implement content safety guardrails: grounding checks and content filtering mandatory for all customer-facing agents

Act as the quality and cost-economics gatekeeper for all agents entering pre-production such as running standardised eval suites enforcing agent best practices and validating inference cost-per-run against approved thresholds before any agent is cleared for production deployment

Collaborate with DevOps (owners of CI/CD automation) through a well-defined infrastructure contract and build version promotion pipeline (e.g. GitHub Actions)

Unleash your potential: What you will be doing and owning:

78 years of hands-on Machine Learning / AI Engineering experience with at least 3 years focused on production GenAI or LLM systems

Masters in Computer Science Machine Learning or a closely related field

Demonstrated experience designing and building multi-tenant production-grade ML platforms or developer-facing AI infrastructure

Proven track record of shipping ML systems end-to-end from architecture and deployment through to operational observability eval pipelines and cost management

Technical Skills

AWS (Deep): API Gateway Amazon Bedrock AgentCore Lambda S3 CloudFront EKS (Kubernetes IRSA) OpenSearch Serverless KMS SageMaker Step Functions WAF PrivateLink CloudWatch IAM

GenAI & LLM Frameworks: LangChain LangGraph LlamaIndex Hugging Face Transformers OpenAI API Anthropic API AWS-native GenAI services

RAG & Knowledge Systems: Vector stores (FAISS ChromaDB Pinecone Weaviate) OpenSearch Serverless - designing end-to-end ingestion chunking embedding and hybrid retrieval pipelines

ML Ops: MLflow (tracking model registry job orchestration) Databricks (PySpark Delta Lake notebooks) automated retraining workflows

Programming: Expert-level Python (FastAPI / Flask / Django); proficient TypeScript / (Express / NestJS); clean code OOP scalable architecture

Data Stores: Delta Lake Elasticsearch Redis NoSQL columnar stores (Databricks BigQuery)

CI/CD & DevOps Collaboration: GitHub Actions; familiarity with Terraform Cloud / HCP Terraform workflows; container-native development on EKS

Observability & Monitoring: CloudWatch (metrics logs dashboards alarms) distributed tracing (AWS X-Ray or equivalent) LLM evaluation frameworks (LangSmith Ragas or similar)

Security & Policy: Policy-as-code frameworks IAM policy design multi-tenant namespace isolation PCI DSS and GDPR technical controls

Soft Skills

Exceptional written and verbal communication - you can explain a policy-as-code enforcement model to a security auditor and a RAG pipeline to a product manager in the same meeting

Strong collaboration instincts - platform engineers serve internal customers; you measure success by adoption not just uptime

Emerging leadership skills - you have mentored engineers led design reviews or owned a technical domain end-to-end

Comfort with ambiguity - this is a greenfield platform with real architectural decisions to make not a maintenance role

Flexibility to occasionally collaborate with teams in California (PST) and Toronto (EST) for cross-time zone syncs

Bonus Points

Familiarity with restaurant hospitality or retail domains

Experience building or fine-tuning domain-specific LLMs or embedding models

Experience building enterprise-scale Text-to-SQL applications

Multi-agent orchestration and A2A protocols; familiarity with workflow automation tools (e.g. n8n) for MCP-compatible tool interoperability

Experience building LLM-specific observability beyond standard infra monitoring e.g. hallucination tracking groundedness scoring per-session cost and cost attribution

Contributions to open-source ML or GenAI projects

Experience with policy-as-code frameworks for cloud access control at scale

Familiarity with defining and executing sprint plans technical roadmaps managing weekly standups and quarterly OKRs for a team

Prior people management or tech lead experience

Interview Process:

Interview #1: Phone Screen with Talent Acquisition Team

Interview #2: Video interview with the Technical Teams (via MS Teams/F2F)

Interview #3: Video interview with the Hiring Manager (via MS Teams/F2F)

PAR is proud to provide equal employment opportunities (EEO) to all employees and applicants for employment without regard to race color religion sex national origin age disability or genetics. We also provide reasonable accommodations to individuals with disabilities in accordance with applicable laws. If you require reasonable accommodation to complete a job application pre-employment testing a job interview or to otherwise participate in the hiring process or for your role at PAR please contact .If youd like more information about your EEO rights as an applicant please visit the US Department of Labors website.

Required Experience:

Senior IC