AI Platform Engineer - PairSoft

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.

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.

• 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.

• 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.

• 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.

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