About this role
Key Responsibilities
• Develop and deploy AI agents using Python and modern agentic frameworks such as LangChain, AutoGen, CrewAI, Haystack, or custom-built solutions. • Design and implement A2A (Agent-to-Agent) workflows, enabling agents to collaborate, reason, negotiate, and complete end-to-end tasks autonomously. • Fine-tune, evaluate, and integrate Large Language Models (LLMs) including OpenAI, Anthropic, Llama, Mistral, and other foundation models. • Build and manage retrieval-augmented systems using vector databases, knowledge graphs, embeddings, and long-term memory mechanisms. • Develop scalable backend services to support AI workloads using FastAPI, Flask, Docker, and cloud platforms (AWS/GCP/Azure). • Integrate external tools and capabilities such as APIs, databases, workflow engines, and custom toolchains for agent skills. • Optimize model inference for performance, latency, throughput, and cost efficiency across different deployment environments. • Collaborate closely with product, engineering, and research teams to bring agentic AI features from ideation through development and production deployment.
Key Responsibilities
• Develop and deploy AI agents using Python and modern agentic frameworks such as LangChain, AutoGen, CrewAI, Haystack, or custom-built solutions. • Design and implement A2A (Agent-to-Agent) workflows, enabling agents to collaborate, reason, negotiate, and complete end-to-end tasks autonomously. • Fine-tune, evaluate, and integrate Large Language Models (LLMs) including OpenAI, Anthropic, Llama, Mistral, and other foundation models. • Build and manage retrieval-augmented systems using vector databases, knowledge graphs, embeddings, and long-term memory mechanisms. • Develop scalable backend services to support AI workloads using FastAPI, Flask, Docker, and cloud platforms (AWS/GCP/Azure). • Integrate external tools and capabilities such as APIs, databases, workflow engines, and custom toolchains for agent skills. • Optimize model inference for performance, latency, throughput, and cost efficiency across different deployment environments. • Collaborate closely with product, engineering, and research teams to bring agentic AI features from ideation through development and production deployment.
Key Responsibilities
• Develop and deploy AI agents using Python and modern agentic frameworks such as LangChain, AutoGen, CrewAI, Haystack, or custom-built solutions. • Design and implement A2A (Agent-to-Agent) workflows, enabling agents to collaborate, reason, negotiate, and complete end-to-end tasks autonomously. • Fine-tune, evaluate, and integrate Large Language Models (LLMs) including OpenAI, Anthropic, Llama, Mistral, and other foundation models. • Build and manage retrieval-augmented systems using vector databases, knowledge graphs, embeddings, and long-term memory mechanisms. • Develop scalable backend services to support AI workloads using FastAPI, Flask, Docker, and cloud platforms (AWS/GCP/Azure). • Integrate external tools and capabilities such as APIs, databases, workflow engines, and custom toolchains for agent skills. • Optimize model inference for performance, latency, throughput, and cost efficiency across different deployment environments. • Collaborate closely with product, engineering, and research teams to bring agentic AI features from ideation through development and production deployment.