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Remote-friendly and Bangalore metro increase applicant pool, but seniority and niche agent/quantization skills limit competition.
Highly specialized LLM agent, quantization, and ML-infrastructure skills reduce cross-industry transferability.
Explicit 10+ years and mandatory ML-infra, agent loop, and quantization expertise create stringent shortlisting filters.
Own and develop the entire agent harness architecture responsible for running AI agents on files, tools, and documents with managed permissions, memory, and execution orchestration.
Build unique components such as session semantics, enforced permissions, memory management, orchestrators, and outputs not available in open-source alternatives.
Maintain compatibility with evolving AI models including adapters, prompt formats, tool-call schemas and build robust validation, retries, and fallback mechanisms for tool usage.
10+ years of software engineering experience in backend systems or ML infrastructure.
Proficient in Python and at least one systems programming language (Go, Rust, C++).
Experience shipping and operating production agent loops involving tool use, multi-step workflows, and unattended runs.
Experience with retrieval-augmented generation (RAG), context management, sandboxing, isolation, and permission models; experience running open-weight AI models and familiarity with quantization and serving impacts.
Experienced in developing complex AI agent orchestration systems with a strong emphasis on backend architecture and security.
Comfortable defining technical roadmaps and product direction in ambiguous and fast-paced environments.
Familiar with evaluation harnesses, benchmarking, and building resilient AI tooling that supports varied AI model behaviors and multi-modal workflows.