AI Agent Algorithm Engineer

Hammerjack Pty Ltd; Philippines; On-site; PHP 900,000 - 2,000,000; Full time

Job summary

Hammerjack Pty Ltd is seeking an experienced AI Engineer to build core Agent logic, including task planning, orchestration, and tool calling for production systems. You will lead data preparation for training, design memory architectures, and advance multimodal RAG pipelines to support robust, immersive user interactions.

The role requires hands-on Python development, 2+ years in multi-agent LLM projects, and collaboration across teams to drive research-to-deployment cycles.

Qualifications

• Master's degree or above in Artificial Intelligence, Computer Science, Mathematics, or related field.
• At least 2 years of full-time industry experience building and deploying production multi-agent LLM systems.
• Hands-on experience fine-tuning LLMs via SFT and DPO, with RAG/retrieval systems.
• Proficient in Python and capable of end-to-end development.
• Strong problem solving, curiosity in frontier AI, and collaboration across teams.
• Experience with agent frameworks such as LangGraph, Google ADK, OWL, or AutoGen.

Responsibilities

• Build core Agent logic: planning, orchestration, tool calls, memory, and multi-agent collaboration.
• Lead pre- and post-training for vertical domains, data pipelines, and RL reward models.
• Design memory architectures for long contexts and immersion in interactions.
• Develop multimodal RAG systems: recall, ranking, long-text processing, multi-document synthesis.
• Develop Agent's tool layer: external APIs, code interpreters, browsers, sandboxes, third-party services.
• Design prompts and context management tailored to product needs.
• Create evaluation plans and monitor metrics for ongoing optimization.
• Explore innovative AI applications.

Skills

Python

LLM systems

Problem solving

Team collaboration

Research to deployment

Education

Master's degree or higher in AI/CS/Math

Tools

LangGraph

Google Agent Development Kit

OWL

AutoGen

Job description

Job Description:

• Build core Agent logic, including but not limited to task planning and orchestration, tool calling, multi-turn dialogue management, memory, RAG, context engineering, and multi-agent collaboration.
• Lead Continuous Pre-training and Post-training for vertical domains and business scenarios, including building high-quality datasets and data pipelines, designing RL reward models, improving instruction following and reasoning capabilities, task completion, role-playing, anthropomorphic and personalized dialogue, proactive/reactive immersive multimodal conversation experiences, and enhancing the model's IQ and EQ.
• Build long-term and short-term memory architectures, addressing issues such as forgetting and attention dispersion in long contexts, and improving immersion and consistency in long-term user interactions.
• Build multimodal RAG systems, including development and optimization of key modules such as recall, ranking, long-text processing, and multi-document synthesis.
• Develop the Agent's tool layer, integrating external APIs and MCP such as search, code interpreters, browsers, sandboxes, and third-party services.
• Design and tune prompts and context management, with tailored optimization for different product requirements.
• Design scientifically rigorous quantitative evaluation systems and plans aligned with product requirements continuously monitor product metrics and provide guidance for Agent and model optimization.
• Explore innovative AI applications.

Requirements:

• Master's degree or above in Artificial Intelligence, Computer Science, Mathematics, or a related field.
• At least 2 years of full-time industry experience building and deploying production multi-agent LLM systems (task planning, orchestration, tool calling).
• Hands-on experience fine-tuning LLMs via SFT and DPO, combined with hands-on experience building and optimizing RAG/retrieval systems (recall, ranking, embedding fine-tuning).
• Good programming skills proficient in Python
• Good problem solving analysis and resolution skills sustained interest and curiosity in frontier AI technologies and applications strong self-drive able to collaborate closely with teams to drive a full closed loop from research to deployment.
• Good development experience with Agent frameworks such as LangGraph, Google Agent Development Kit, OWL, or AutoGen.