AI Platform Engineer

About This Opportunity

We are representing a confidential SaaS organization that is building a centralized AI services platform from the ground up. This is a hands-on engineering role on a founding platform team—you will architect and ship the v1 platform that every product line in the portfolio will consume. You will make daily technical decisions that shape how AI is delivered to real customers on short cycles, working alongside a small team of senior engineers in a fully remote capacity from India.

Responsibilities

Build & Ship Platform Services

• Design, build, and operate core services in the central AI platform: model gateway, RAG-as-a-service, evals and observability, guardrails, and cost tagging
• Write production Python for AI services; contribute to shared libraries, SDKs, and integration patterns for product teams
• Instrument every service with cost tagging per request, latency and error metrics, quality signals, and audit logs
• Own on-call rotation for AI platform services you build; author and improve runbooks after every incident

Collaborate Across Teams

• Work directly with product engineering leads to onboard AI features onto the central platform
• Provide technical support, integration guidance, and troubleshooting to consuming teams
• Contribute to Architecture Decision Records; document tradeoffs and push back on decisions when necessary

Set the Operational Bar

• Own observability, alerting, incident response, and post-incident reviews
• Evaluate vendors and tools in your area; run bakeoffs and make cost, quality, and reliability tradeoffs explicit
• Contribute to AI security posture: PII handling, tenant isolation, prompt injection defense, and audit logging

Applied AI & RAG Engineering

• Build 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
• Develop guardrails and content safety: input filtering, output validation, PII redaction, tool-use sandboxing
• Support agent frameworks and tool-use patterns as workflows move into production
• Run domain-specific fine-tuning experiments and quality benchmarking

Backend & Platform Engineering

• Build multi-provider model gateway with routing, fallback, retry, and rate-limit logic
• Create prompt registry, versioning, and rollout controls (canary, feature flags)
• Develop shared libraries and SDKs with clear API contracts, versioning, and deprecation strategy
• Design tenant isolation architecture for safe customer data flows
• Implement cost attribution and budget enforcement at the gateway layer

MLOps/LLMOps Engineering

• Build observability platform: prompt and response tracing, cost per request, quality signals, drift detection
• Create evaluation infrastructure: golden datasets, offline evals, LLM-as-judge patterns, regression testing
• Own model deployment pipelines, including fine-tuned models where applicable
• Establish alerting and SLO framework for AI services with quality regression as first-class alerts
• Build fine-tuning and RLHF pipelines when product-specific tuning is justified

What We're Looking For

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or equivalent
• 5+ years of professional software engineering experience with 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 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 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 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; able to write 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 full ML lifecycle: training pipelines, serving infrastructure, 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 across distributed time zones
• 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

How We Work With You

When you apply through Get A Job.ai, our recruiting team will review your profile and conduct an initial screening. If there's a strong match, we will submit your candidacy to our client for consideration. Please do not contact the client directly—all communication and coordination will flow through our talent team to ensure the best experience for you throughout the process.

Pay

Compensation details will be discussed during the screening process and are competitive with the Indian market for senior platform engineering roles.

Equal Employment Opportunity

Get A Job.ai is an equal opportunity employer. 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 applicable law.