The selected candidate will be working for our Indian JV - D2AI Labs. The location is Bangalore/Chennai/Remote. Responsibilities Key Responsibilities: • Lead Agent Development: Architect, build, and deploy the central interface agent that connects and coordinates specialized AI agents. • Agent Orchestration: Implement reliable communication, delegation, and data-handling mechanisms between agents. • LLM Integration: Fine-tune and integrate LLMs (Azure OpenAI or similar), utilizing advanced prompt engineering techniques. • MLOps Implementation: Maintain robust MLOps pipelines, CI/CD workflows, and performance monitoring on Azure. • Vector Database Management: Design data pipelines using vector databases like Pinecone, Qdrant, or Milvus for RAG and agent memory. • A2A Communication & MCP: Enable advanced agent-to-agent communication using established protocols like MCP/MCPTx. • Collaborate with cross-functional teams to translate business requirements into scalable AI/ML solutions. • Optimize AI models for performance, scalability, and accuracy. • Conduct thorough testing, evaluation, and documentation of models and systems. • Stay up to date with the latest advancements in AI, particularly in the Generative AI space. Qualifications & Skills : • Proven experience in LLM model development (e.g., OpenAI, GPT, BERT, etc.). • Hands-on experience in building GenAI agents and conversational workflows. • Strong Python programming skills with a focus on AI/ML libraries and frameworks (e.g., PyTorch, TensorFlow, Hugging Face). • Proven experience building or orchestrating multi-agent systems using frameworks like LangGraph. • Prior work experience in the Telecom industry with a clear understanding of domain-specific challenges.
The selected candidate will be working for our Indian JV - D2AI Labs in Bangalore/Chennai/Remote. Key Responsibilities: Lead Agent Development: Architect, build, and deploy the central interface agent that connects and coordinates specialized AI agents. Agent Orchestration: Implement reliable communication, delegation, and data-handling mechanisms between agents. LLM Integration: Fine-tune and integrate LLMs (Azure OpenAI or similar), utilizing advanced prompt engineering techniques. MLOps Implementation: Maintain robust MLOps pipelines, CI/CD workflows, and performance monitoring on Azure. Vector Database Management: Design data pipelines using vector databases like Pinecone, Qdrant, or Milvus for RAG and agent memory. A2A Communication & MCP: Enable advanced agent-to-agent communication using established protocols like MCP/MCPTx. Collaborate with cross-functional teams to translate business requirements into scalable AI/ML solutions. Optimize AI models for performance, scalability, and accuracy. Conduct thorough testing, evaluation, and documentation of models and systems. Stay up to date with the latest advancements in AI, particularly in the Generative AI space. Qualifications & Skills: Proven experience in LLM model development (e.g., OpenAI, GPT, BERT, etc.). Hands-on experience in building GenAI agents and conversational workflows. Strong Python programming skills with a focus on AI/ML libraries and frameworks (e.g., PyTorch, TensorFlow, Hugging Face). Proven experience building or orchestrating multi-agent systems using frameworks like LangGraph. Prior work experience in the Telecom industry with a clear understanding of domain-specific challenges.,