AI Platform Engineer

Job Title: AI Platform Engineer Location: Pan India ( remote) Timings :11 am to 8pm Job Summary This is a hands-on build role on a new team. PairSoft is standing up a central AI services platform that will be consumed by every product line in the portfolio. You will build the platform, not maintain a legacy stack. You will make daily technical decisions that shape how AI is delivered across the company, and you will see your work in production, in front of real customers, on a short cycle. You will work alongside two or three other senior engineers on the founding platform team. In the first three to six months you will focus on shipping the v1 platform: model gateway, RAG-as-a-service, evals and observability, guardrails, and cost tagging. You will help migrate existing product AI features onto the platform and harden the operational surface. Location : Remote (anywhere from India) Responsibilities / Essential Functions Build Design, build, and operate services in the central AI platform. Every service should have clear API contracts, versioning, and SLOs from day one. Write production Python for AI services. Contribute to shared libraries, SDKs, and integration patterns that product teams will consume. Instrument everything: cost tagging per request, latency and error metrics, quality signals, and audit logs. If it is not measured, it is not shipped. Own on-call rotation for the AI platform services you build. Author runbooks and improve them after every incident. Partner Work directly with product engineering leads across the product lines to onboard their AI features onto the central platform. Provide technical support, integration guidance, and troubleshooting to product teams consuming platform services. Contribute to Architecture Decision Records. Push back on decisions you disagree with; document tradeoffs. Operate Set the operational bar: observability, alerting, incident response, and post-incident reviews. Own vendor evaluation for tools in your area of specialization. Run bakeoffs when the choice is not obvious. Make cost, quality, and reliability tradeoffs explicit. Contribute to the AI security posture: PII handling, tenant isolation, prompt injection defense, and audit logging within your services. Applied AI & RAG Engineering The AI-forward end of the platform. You build the retrieval, prompt, and guardrail systems that make LLM output good enough to ship to customers. RAG-as-a-service platform: ingestion, chunking, embedding, retrieval quality, and hybrid search. Prompt engineering at scale: templates, evaluation, versioning, and per-tenant customization. Guardrails and content safety: input filtering, output validation, PII redaction, tool-use sandboxing. Agent frameworks and tool-use patterns as agent workflows move into production across product lines. Domain-specific fine-tuning experiments and quality benchmarking. Backend & Platform Engineering The foundation of the AI platform. You own the model gateway, orchestration layer, service mesh, and the data plumbing that ties everything together. Multi-provider model gateway with routing, fallback, retry, and rate-limit logic. Prompt registry, versioning, and rollout controls (canary, feature flags). Shared libraries and SDKs for product-team consumption. API contracts, versioning, deprecation strategy. Tenant isolation architecture: how customer data flows through platform services safely. Cost attribution and budget enforcement at the gateway layer. MLOps/ LLMOps Engineering The operational spine of the AI platform. You own how models are deployed, observed, evaluated, and rolled back safely across the portfolio. Observability platform: prompt and response tracing, cost per request, quality signals, drift detection. Evaluation infrastructure: golden datasets, offline evals, LLM-as-judge patterns, regression testing. Model deployment pipelines, including fine-tuned models where applicable. Alerting and SLO framework for AI services. Distinct from general engineering SLOs: quality regression is a first-class alert. Fine-tuning and RLHF pipelines when product-specific tuning becomes justified. Requirements Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent. 5+ years of professional software engineering experience with a strong production track record. 6+ years building production distributed systems, ideally including internal developer platforms or API gateways at scale. 5+ years in MLOps, LLMOps, ML platform engineering, or a hybrid DevOps plus ML role at production scale. Hands on experience with observability tools for LLM systems: LangSmith, Langfuse, Braintrust, Arize, or comparable. Working knowledge of evaluation methodology for LLM systems: benchmark design, LLM-as-judge, human review workflows Working fluency in the modern LLM ecosystem: OpenAI or Anthropic APIs, at least one orchestration framework (LangChain, LlamaIndex, or equivalent), at least one vector database, at least one observability tool. 2+ years of hands-on production experience with LLM-based systems: prompt engineering, RAG, evaluation, or LLM infrastructure. Strong Python and one of Go or Java. Comfortable with async patterns, backpressure, and rate limiting. Comfortable writing production code, not just notebooks or scripts. Experience designing multi-tenant systems with hard isolation guarantees. Cloud-native depth on Azure or AWS: Kubernetes, service mesh, IaC (Terraform), CI/CD. Experience shipping model updates safely in production: canaries, shadow evaluation, rollback triggers. Comfort with the full ML lifecycle: training pipelines, serving infra, monitoring, and cost management. Strong grasp of AI security fundamentals: PII handling, tenant isolation, prompt injection basics. Ability to communicate technical decisions clearly in async writing. This role is distributed across time zones and cannot be run on synchronous meetings alone. Fluent English language skills Preferred Qualifications Domain experience in procure-to-pay, ERP integration, accounts payable, procurement, or adjacent finance and operations software. Experience at a product company or PE-backed B2B SaaS, ideally on an internal platform team. Contributions to open-source AI/ML infrastructure projects. Experience with agent frameworks (LangGraph, AutoGen, CrewAI, or custom orchestration) in production. Prior experience on a founding platform team where you shipped v1 of a service used by multiple internal customers. Work and Physical Requirements This position may work from home and/or office environment. Working in an office environment may require prolonged sitting, work in an air-conditioned environment, ability to work with others at various noise levels and ability to lift up to 30 pounds or 15 kg. Other Duties This job description is not intended to be a complete list of responsibilities. Other duties may be assigned by management that may reasonably fit in the scope of job. Equal Employment Opportunity Statement PairSoft is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law. About the Company We are a global team of innovators and advocates transforming how financial data is captured, stored, and manipulated with our comprehensive suite of automation technology. Our platform seamlessly integrates with your existing ERP for an unrivaled end-user experience. We do the heavy lifting so accounting, procurement, and fundraising teams can do their best work. PairSoft’s aspires to be the strongest procure-to-pay platform for the mid-market and enterprise, with close integration to Microsoft Dynamics, Blackbaud, Oracle, SAP, Acumatica and Sage ERPs. At PairSoft, we are passionate about innovation, transparency, diversity, and advocating on behalf of our customers and communities we support. We offer exciting career opportunities and a collaborative culture that allows individuals to learn, grow, and create meaningful impact. We are expanding and seeking team players who are eager to jump in and contribute to our rapid growth! PairSoft is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status or any other protected status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please email us at: [email protected]. To read our Candidate Data Privacy Notice - including GDPR - click here.