About the role
Roles & Responsibilities · Lead the design and development of agentic and intelligent systems with strong ownership of core algorithms, model behavior, and decision logic, minimizing reliance on managed AI services and abstraction-heavy frameworks. · Architect end-to-end agentic systems with deep control over model internals, reasoning flows, tool-use logic, and execution pipelines rather than wrapper-based orchestration. · Take algorithmic and model-level ownership of agent behavior, including reasoning strategies, planning algorithms, fallback logic, and failure handling. · Design and implement custom agent orchestration, memory systems, context management, and stateful workflows without over-dependence on LangChain/LangGraph-style abstractions. · Build and fine-tune open-source and self-hosted LLMs or hybrid approaches (open-weight + proprietary) based on performance, cost, and security trade-offs. · Develop custom RAG pipelines, embedding strategies, ranking, re-ranking, and grounding mechanisms tailored to enterprise knowledge systems. · Integrate agentic solutions with enterprise applications, APIs, data platforms, and internal ML systems. · Establish low-level design patterns, reusable libraries, and internal frameworks for building agent-based systems from first principles. · Optimize model inference, reasoning efficiency, latency, cost, and response quality, including prompt-free and programmatic reasoning approaches where applicable. · Drive Responsible AI practices with in-house guardrails, explainability techniques, auditability, and governance controls. · Collaborate with business stakeholders to translate complex requirements into deterministic, reliable agent-driven solutions, avoiding black-box dependencies. · Lead production support, deep debugging, RCA, and performance tuning at both system and model levels. · Mentor junior consultants on core AI concepts, algorithms, and system design, not just framework usage.