AI Platform Engineer

Accountabilities:

• Design, build, deploy, and operate scalable AI platform services with clear API contracts, versioning, service-level objectives, and production-readiness standards.
• Develop production-grade Python services, shared libraries, SDKs, and integration patterns for consumption by multiple product engineering teams.
• Build and evolve a multi-provider model gateway with routing, fallback, retry, rate limiting, cost attribution, and budget enforcement capabilities.
• Develop RAG-as-a-service capabilities covering data ingestion, chunking, embeddings, retrieval, hybrid search, and retrieval-quality measurement.
• Establish prompt management capabilities including templates, versioning, evaluation, rollout controls, and tenant-specific customization.
• Build guardrails and content-safety mechanisms such as input filtering, output validation, PII redaction, and controlled tool execution.
• Develop agent and tool-use patterns suitable for reliable production workflows.
• Build observability capabilities covering prompt and response tracing, latency, errors, cost per request, quality signals, drift detection, and audit logging.
• Design and maintain evaluation infrastructure using benchmark datasets, offline evaluations, LLM-as-judge approaches, human review workflows, and regression testing.
• Build and operate model deployment pipelines, including safe deployment and rollback processes for fine-tuned models where applicable.
• Establish alerting and SLO frameworks for AI services, treating quality regressions as a first-class operational concern.
• Participate in on-call rotations, create incident response runbooks, and continuously improve operational procedures following incidents.
• Collaborate directly with product engineering teams to onboard AI features, provide integration guidance, and troubleshoot platform issues.
• Contribute to architecture decisions, document technical tradeoffs, and constructively challenge approaches when appropriate.
• Evaluate third-party technologies and vendors through structured comparisons, making cost, quality, security, and reliability tradeoffs explicit.
• Strengthen AI security across platform services, including PII protection, tenant isolation, prompt-injection defenses, and auditability.
• Identify opportunities to improve scalability, reliability, developer experience, and operational efficiency across the platform.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent professional experience.
• At least 5 years of professional software engineering experience with a strong production track record.
• Approximately 6+ years of experience building production distributed systems, ideally including internal developer platforms, infrastructure platforms, or API gateways at scale.
• At least 5 years of experience in MLOps, LLMOps, ML platform engineering, or a comparable combination of DevOps and machine-learning engineering at production scale.
• At least 2 years of hands-on production experience with LLM-based systems, including areas such as prompt engineering, RAG, evaluation, or LLM infrastructure.
• Strong Python development skills plus proficiency in Go or Java, with experience writing production-quality code rather than primarily notebooks or scripts.
• Strong understanding of asynchronous programming, backpressure, rate limiting, distributed systems, and production service design.
• Practical experience with LLM observability and evaluation platforms such as LangSmith, Langfuse, Braintrust, Arize, or comparable tools.
• Working knowledge of LLM evaluation methodologies, including benchmark design, LLM-as-judge approaches, regression testing, and human review workflows.
• Familiarity with modern LLM technologies, including OpenAI or Anthropic APIs, an orchestration framework such as LangChain or LlamaIndex, vector databases, and LLM observability tooling.
• Experience designing and operating multi-tenant systems with strong data and service isolation guarantees.
• Strong cloud-native experience with AWS or Azure, including Kubernetes, service mesh technologies, infrastructure-as-code, Terraform, and CI/CD.
• Experience deploying model updates safely using techniques such as canary releases, shadow evaluation, feature controls, and rollback triggers.
• Understanding of the full ML lifecycle, including training pipelines, model serving, monitoring, evaluation, and cost management.
• Strong understanding of AI security fundamentals, particularly PII handling, tenant isolation, prompt-injection risks, and audit logging.
• Strong written and verbal communication skills, with the ability to document technical decisions clearly in an asynchronous, distributed environment.
• Fluent English communication skills.
• Preferred experience in procure-to-pay, ERP integration, accounts payable, procurement, finance, operations software, or related domains.
• Experience working at a product company, B2B SaaS organization, or internal platform team is advantageous.
• Contributions to open-source AI/ML infrastructure projects are a plus.
• Production experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration is desirable.
• Experience as part of a founding platform team responsible for launching a new service used by multiple internal customers is highly valuable.

Benefits:

• Fully remote opportunity available across India.
• Work schedule aligned to 11:00 AM–8:00 PM.
• Opportunity to join a newly formed AI platform team and help build its initial platform from the ground up.
• Significant technical ownership and the opportunity to influence foundational architecture and engineering standards.
• Hands-on exposure to modern LLM, RAG, AI agent, MLOps, LLMOps, cloud, observability, and platform technologies.
• Opportunity to work on production AI systems used across multiple product lines.
• Close collaboration with experienced engineers and product development teams across a distributed environment.
• Broad technical scope spanning applied AI, backend engineering, cloud infrastructure, security, reliability, and developer experience.
• Opportunity to establish best practices for AI reliability, evaluation, observability, security, and cost management.
• Fast-moving environment with a strong emphasis on engineering quality, ownership, learning, and measurable production impact.