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Who we are:
Artmac Soft is a technology consulting and service-oriented IT company dedicated to providing innovative technology solutions and services to Customers.
Job Description:
Job Title : Lead Agentic AI Engineer GenAI & Multi-Agent Systems
Job Type : C2C
Experience : 8 - 15 Years
Location : San Jose California (On-Site)
Required Skills and Experience:
Experience in agents and agentic frameworks.
Proven hands-on experience with Langchain Langgraph LlamaIndex Autogen Crew or similar.
Expertise in Python programming and strong familiarity with agent orchestration libraries.
Solid understanding of AI/ML concepts algorithms and frameworks (e.g. transformers RL LLMs).
Experience integrating solutions with cloud platforms such as AWS (Bedrock) Azure and Google Cloud.
Experience building Internet of Agents (IoA) architectures.
Background in multi-agent systems autonomous decision-making or reinforcement learning.
Experience working in cross-functional agile environments.
Contributions to open-source GenAI/Agentic frameworks.
Ability to lead a team and manage project timelines deliverables and stakeholder communication.
Excellent problem-solving analytical and debugging skills.
Responsibilities:
Design build and deploy agent-based systems using modern GenAI frameworks like Langchain Langgraph LlamaIndex Crew Autogen etc.
Architect multi-agent collaboration workflows ensuring modularity scalability and reusability.
Collaborate with cross-functional teams to integrate agent systems with cloud platforms such as AWS Bedrock Azure AI and Google Vertex AI.
Lead and mentor a team of AI engineers and data scientists fostering a collaborative and innovative environment.
Perform research and experimentation to enhance agent capabilities and system performance.
Write clean scalable and high-performance Python code with a strong focus on agentic orchestration.
Conduct code reviews debugging and testing to ensure high-quality deliverables.
Stay current with industry trends and advancements in agent technologies GenAI and ML.
Full-time