Responsibilities
- Lead full lifecycle development of machine learning models, including data preparation, training, testing, deployment, and ongoing monitoring.
- Design and implement generative AI systems such as retrieval-augmented generation, agent-based workflows, MCP servers, and conversational AI interfaces that support organizational objectives.
- Collaborate with data engineering to create and manage data pipelines, feature repositories, and workflow orchestration tools.
- Integrate trained AI models into live environments using APIs, microservices, and cloud infrastructure.
- Enhance model efficiency, reliability, scalability, and cost-effectiveness across production workloads.
- Track deployed model behavior for signs of performance degradation, bias, fairness issues, or reliability concerns, and apply corrective measures.
- Maintain thorough documentation of model architecture, experimental results, data lineage, and design decisions.
- Keep current with advancements in AI research, libraries, and platforms—including tools like LangChain, LangGraph, MCP components, Agents SDKs, and LiveKit—and suggest applicable innovations.
- Engage directly with clients to define challenges, convert business needs into technical AI strategies, and present outcomes clearly.
Equal employment opportunity
The organization is committed to fair hiring practices and does not discriminate on the basis of race, color, religion, caste, age, gender, national origin, disability status, genetic information, veteran status, sexual orientation, gender identity or expression, or any other trait protected by federal, state, local, or international laws.
Applicants
Individuals needing support during the application or interview stages may contact careers@Particle41.com for help.
