Senior AI/ML Engineer

Tech Economy is a leading consulting firm recognized as one of the world’s best places to work. They are seeking a Senior AI/ML Engineer to build and operate production ML systems, collaborate with data scientists and engineers, and mentor mid-level engineers.

Responsibilities:

• Build, deploy, and operate production inference and serving systems for models, embeddings, and re-rankers: request batching, concurrency, and throughput tuning against latency and cost SLAs
• Own the model and prompt lifecycle in MLflow: packaging, model registry governance, promotion workflows, staged rollout behind feature flags, and clean rollback
• Build and maintain LLMOps tooling: prompt and instruction versioning, model-gateway configuration (e.g., Portkey), inference orchestration, and response caching and cost controls
• Build and operate production RAG and retrieval pipelines end-to-end: structure-aware chunking, contextual embedding, hybrid vector plus keyword retrieval, cross-encoder re-ranking, and context assembly
• Design and maintain model and retrieval evaluation frameworks: golden datasets, metric definitions, LLM-as-judge with calibration, regression gates in CI, and production drift monitoring
• Instrument production ML systems with structured logs, OpenTelemetry spans, and Prometheus metrics: token usage, latency percentiles, retrieval hit rates, drift, and hallucination monitoring, with dashboards and alerting
• Collaborate with the Agent / AI squad to serve model and retrieval outputs as structured tool responses consumed by the Agent Gateway; partner with Data Engineers on feature and embedding pipelines
• Drive production ML incident response to resolution; treat deployment, monitoring, and maintenance as part of delivery
• Set and enforce engineering standards for ML and serving code; contribute to repository conventions and raise the bar for production practices
• Mentor mid-level ML Engineers and Data Scientists on production ML and LLMOps practice; conduct thorough code reviews and enforce standards on PRs
• Use AI coding assistants to accelerate pipeline scaffolding, evaluation-harness development, and serving-config authoring; review all generated code against production standards before committing
• Use LLMs to generate first-draft documentation, runbooks, and evaluation reports; validate and refine outputs before publishing