Senior Staff Engineer, Machine Learning (AI Engineering)

TL;DR

Senior Staff Engineer, Machine Learning (AI Engineering): Designing and building backend services powering AI/ML runtime including orchestration, session/state management, and tools/services integration with an accent on performance, reliability, and cost optimization. Focus on translating business requirements into scalable technical designs and driving troubleshooting and root-cause analysis.

Location: South Asia

Company

hirify.global is a Digital Product Engineering company that builds products, services, and experiences.

What you will do

• Design and build core backend services powering AI/ML runtime including orchestration, session/state management, and tools/services integration.
• Implement end-to-end retrieval and memory systems covering ingestion, embeddings, indexing, vector search, ranking, caching, and lifecycle management.
• Productionize ML workflows with feature/metadata services, model integration contracts, and evaluation hooks.
• Drive performance, reliability, and cost optimization with strong SLO ownership and observability practices (logs, metrics, tracing, guardrails).
• Collaborate with applied ML teams on model routing, prompts/tools, evaluation datasets, and safe releases.
• Translate business requirements into scalable technical designs, define NFR benchmarks, and review architecture for extensibility and best practices.

Requirements

• Total experience of 7.5 years+.
• Strong expertise in Python and backend engineering with experience building scalable, distributed microservices.
• Hands-on experience designing and delivering end-to-end RAG (Retrieval-Augmented Generation) workflows in production systems.
• Solid understanding of ML solution design, including embeddings, retrieval, ranking, feature engineering, and evaluation strategies.
• Experience with vector databases and implementing chunking, indexing, vector search, re-ranking, caching, and memory patterns.
• Strong backend integration skills using REST/gRPC APIs, Docker, Kubernetes, CI/CD, and cloud platforms (AWS/GCP/Azure).