AI Platform Engineer
PairSoft
IndiaremotePosted 1 month ago
Skill Required
AI-Platform-EngineeringAI-ML-InfrastructurePlatform-EngineeringLLMOpsBackend-EngineeringAI-Platform-EngineerPlatform-AI-EngineerAI-ML-Platform-EngineerLead-AI-Platform-EngineerSenior-AI-Platform-EngineerAI-Agent-Platform-EngineerAI-Data-Platform-EngineerAI-Platform-ArchitectMachine-Learning-Platform-EngineerAI EngineerPlatform EngineerAIsoftware engineeringSystem DesignMicrosoft DynamicsGenerative AIManual TestingSOCRAGtroubleshootingObservabilityIstioEngineeringKubernetesautomationLangChainTerraformdesigningsecuritybuildingTestNGPythonOracledesignDevOpsOpenAPIFulltime
Key highlights
- 5+ years of professional software engineering experience.
- 6+ years building production distributed systems at scale.
- 5+ years in MLOps/LLMOps or hybrid DevOps/ML roles.
- Strong Python and one of Go or Java required.
- Collaborative culture with learning and growth opportunities.
- Equal opportunity workplace with diversity and inclusion commitment.
Role overview
Responsibilities
- 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.
- Build the retrieval, prompt, and guardrail systems that make LLM output good enough to ship to customers.
- Develop RAG-as-a-service platform: ingestion, chunking, embedding, retrieval quality, and hybrid search.
- Implement prompt engineering at scale: templates, evaluation, versioning, and per-tenant customization.
- Create guardrails and content safety: input filtering, output validation, PII redaction, tool-use sandboxing.
- Develop agent frameworks and tool-use patterns as agent workflows move into production across product lines.
- Execute domain-specific fine-tuning experiments and quality benchmarking.
- Design multi-provider model gateway with routing, fallback, retry, and rate-limit logic.
- Manage prompt registry, versioning, and rollout controls (canary, feature flags).
- Maintain shared libraries and SDKs for product-team consumption. API contracts, versioning, deprecation strategy.
- Architect tenant isolation: how customer data flows through platform services safely.
- Implement cost attribution and budget enforcement at the gateway layer.
- Engineer observability platform: prompt and response tracing, cost per request, quality signals, drift detection.
- Build evaluation infrastructure: golden datasets, offline evals, LLM-as-judge patterns, regression testing.
- Create model deployment pipelines, including fine-tuned models where applicable.
- Define alerting and SLO framework for AI services, distinct from general engineering SLOs.
- Develop 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.
Nice to have
- 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.
Benefits
- Exciting career opportunities with a collaborative culture that allows individuals to learn, grow, and create meaningful impact.
- Company values of innovation, transparency, diversity, and advocacy for customers and communities.
- Equal opportunity workplace committed to diversity and inclusion.
- Accommodation support for disabilities or special needs.
- Global team of innovators transforming financial data automation.
- Opportunity to work on a fast‑growing platform with rapid growth prospects.
Additional details
- 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.
- PairSoft 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.
- 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: .
- To read our Candidate Data Privacy Notice - including GDPR - click here.
- Originally posted on Himalayas.