AI Platform Engineer

Role overview

Accountabilities

• Design, build, deploy, and operate scalable AI platform services with API contracts, versioning, SLOs, and production-readiness standards.
• Develop production-grade Python services, shared libraries, SDKs, and integration patterns.
• Build a multi-provider model gateway with routing, fallback, retry, rate limiting, cost attribution, and budget enforcement.
• Develop RAG-as-a-service covering ingestion, chunking, embeddings, retrieval, hybrid search, and quality measurement.
• Establish prompt management, guardrails, content safety, and reliable agent and tool-use patterns.
• Build observability for tracing, latency, errors, cost, quality, drift, and audit logging.
• Maintain evaluation infrastructure with benchmarks, LLM-as-judge methods, human review, and regression testing.
• Operate model deployment pipelines with safe release and rollback processes.
• Establish alerting and SLO frameworks, participate in on-call rotations, and improve incident response.
• Partner with product engineering teams on onboarding, integration guidance, troubleshooting, architecture, vendor evaluation, and platform improvement.

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 or more years building production distributed systems, ideally platforms or API gateways at scale.
• At least 5 years in MLOps, LLMOps, ML platform engineering, or comparable production DevOps and machine-learning engineering.
• At least 2 years of hands-on production experience with LLM-based systems.
• Strong Python plus Go or Java development skills for production-quality code.
• Strong understanding of asynchronous programming, backpressure, rate limiting, distributed systems, and service design.
• Experience with LLM observability, evaluation methodologies, multi-tenant systems, and isolation guarantees.
• Cloud-native experience with AWS or Azure, Kubernetes, service mesh, Terraform, infrastructure-as-code, and CI/CD.
• Experience with safe model deployment techniques and understanding of the full ML lifecycle.
• Strong AI security knowledge covering PII, tenant isolation, prompt injection, and audit logging.
• Fluent English communication skills.

Benefits and work environment

• Fully remote opportunity available across India.
• Work schedule aligned to 11:00 AM–8:00 PM.
• Join a newly formed AI platform team with significant technical ownership.
• Gain exposure to modern LLM, RAG, AI agent, MLOps, LLMOps, cloud, observability, and platform technologies.
• Work on production AI systems used across multiple product lines in a distributed engineering environment.