Job Description: 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.
Responsibilities: 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.
Qualifications: 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.
