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 100,000 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 partech.com 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 7–8 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 We’re 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, tenant_id 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:
7–8 years of hands-on Machine Learning / AI Engineering experience, with at least 3 years focused on production GenAI or LLM systems
Master's 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 / Node.js (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 [email protected]. If you’d like more information about your EEO rights as an applicant, please visit the US Department of Labor's website.
