Assistant Manager / Manager

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.