Lead Software Engineer
We are seeking an experienced and visionary Senior AI Developer specializing in Generative AI, Agentic AIArchitectures, and Enterprise Retrieval-Augmented Generation (RAG) systems. In this role, you will be responsible for designing, building, and deploying cutting-edge AI-powered solutions, multi-agent workflows,and intelligent semantic retrieval pipelines that solve complex enterprise challenges.
You will lead the technical design and end-to-end implementation of scalable Large Language Model (LLM)applications, autonomous agentic systems, vector search backends, and robust evaluation guardrails.
Responsibilities:
1. Generative AI & LLM Engineering
• Architect, fine-tune, and deploy foundation model applications utilizing commercial APIs (e.g., OpenAI,Anthropic, Google Vertex AI) and open-source models (e.g., Llama, Mistral, Qwen).
• Design robust prompt engineering patterns, structured output schemas (JSON/Pydantic validation), modelrouting, and context window management strategies.
• Implement optimization techniques including parameter-efficient fine-tuning (PEFT/LoRA), quantization,caching, and model distillation for latency and cost efficiency.
2. Agentic AI & Autonomous Systems
• Design and orchestrate deterministic and autonomous multi-agent workflows using modern frameworks (e.g.,LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom state-machine engines).
• Build sophisticated agent capabilities including tool use/function calling, multi-step planning, reflection/self-correction loops, memory management (short-term, long-term, working memory), and human-in-the-loop(HITL) approval gates.Implement protocol standards such as Model Context Protocol (MCP) and OpenAPI-driven tool execution.
You will lead the technical design and end-to-end implementation of scalable Large Language Model (LLM)applications, autonomous agentic systems, vector search backends, and robust evaluation guardrails.
Responsibilities:
1. Generative AI & LLM Engineering
• Architect, fine-tune, and deploy foundation model applications utilizing commercial APIs (e.g., OpenAI,Anthropic, Google Vertex AI) and open-source models (e.g., Llama, Mistral, Qwen).
• Design robust prompt engineering patterns, structured output schemas (JSON/Pydantic validation), modelrouting, and context window management strategies.
• Implement optimization techniques including parameter-efficient fine-tuning (PEFT/LoRA), quantization,caching, and model distillation for latency and cost efficiency.
2. Agentic AI & Autonomous Systems
• Design and orchestrate deterministic and autonomous multi-agent workflows using modern frameworks (e.g.,LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom state-machine engines).
• Build sophisticated agent capabilities including tool use/function calling, multi-step planning, reflection/self-correction loops, memory management (short-term, long-term, working memory), and human-in-the-loop(HITL) approval gates.Implement protocol standards such as Model Context Protocol (MCP) and OpenAPI-driven tool execution.