Rekruton Technologies

What You'll Do ● Lead the design and implementation of complex AI systems spanning models, retrieval, tools, agents, data, APIs, and existing services. ● Own AI capabilities end-to-end from technical discovery and architecture through deployment, production operations, and continuous&nbsp;improvement. ● Design scalable RAG and retrieval architectures, context and memory strategies, agent/tool workflows, and model-provider integration&nbsp;patterns. ● Drive model and architecture decisions using systematic evaluation of quality, latency, reliability, security, scalability, and cost. ● Define evaluation strategies, golden datasets, regression suites, observability, and operational SLOs for production AI systems. ● Design resilience patterns including fallbacks, caching, retries, human review, failure recovery, and safe degradation. ● Identify and build shared components that should be standardized across pods rather than repeatedly implemented. ● Lead technical design reviews, document architecture and trade-offs, and partner with Staff Engineers/Solution Advisory Board on&nbsp;cross-platform implications. ● Mentor AI Engineers I/II and raise engineering quality through code/design reviews and technical coaching. ● Evaluate emerging models, protocols, frameworks, and platform capabilities and recommend adoption when they create measurable value.
 Technical Skills &amp; Technology Stack ● Programming &amp; Systems: Advanced Python and strong software/system design fundamentals; ability to integrate with Java or&nbsp;JavaScript/TypeScript services as needed. ● Data: Strong SQL, Snowflake or equivalent data platforms, data pipelines, structured/unstructured data, and retrieval-oriented data design. ● Generative AI / LLMs: Deep hands-on experience with multiple model providers, tool/function calling, structured generation, context&nbsp;engineering, and model-selection strategies. ● AI Architecture: Production RAG, vector/hybrid search, ranking, agents, tool orchestration, context/memory patterns, and human-in-the-loop workflows. ● Protocols &amp; Frameworks: LangGraph/LangChain/LlamaIndex or equivalent; MCP/tool integration patterns; ability to design abstractions&nbsp;independent of individual frameworks/providers. ● Cloud &amp; Platform: AWS or equivalent, containers, CI/CD, service observability, distributed system fundamentals, and production operations. ● Production AI: Evaluation frameworks, tracing/observability, guardrails, security, caching, fallbacks, latency/cost optimization, and&nbsp;failure-mode analysis.