Design and prototype agent workflows using LangChain and/or similar frameworks.
Develop task automation agents focused on marketing workflows (e.g., campaign execution, reporting, content validation).
Build basic RAG pipelines and ReAct agents to improve agent response relevance and accuracy.
Benchmark and test LLMs for speed, cost, and fitness to the use case.
Work closely with the Hiring Manager To iterate on use cases and validate the POC.

What You Need to Succeed
Must-haves

3+ years of experience in software engineering or machine learning, with at least 1 year of hands-on experience building LLM-based workflows or autonomous agents.

Proven ability to design, prototype, and iterate on solutions using LangChain or similar frameworks.
Strong understanding of prompt engineering, tool-use chaining, and agent orchestration patterns (e.g., ReAct, Tool-Calling).
Practical experience working with vector databases such as Pinecone, FAISS, or Weaviate, and building Retrieval-Augmented Generation (RAG) pipelines.
Proficiency in Python and relevant ML libraries (e.g., Transformers, PyTorch, LangChain).
Familiarity with Docker and experience deploying ML workloads in cloud environments (AWS, GCP, or Azure).
Ability to work independently, communicate effectively with non-technical stakeholders, and iterate rapidly in a PoC-driven environment.
Nice-to-haves
Experience with multi-agent frameworks like LangGraph, AutoGen, or CrewAI.
Previous work on agentic systems applied to marketing automation or related use cases.
Knowledge of CI/CD practices, container orchestration, and performance monitoring for ML pipelines.
Awareness of AI ethics, security, and compliance in production-grade applications.
Demonstrated contributions to open-source projects, published research, or developer communities in the AI/LLM space.

Originally posted on Himalayas

AI Engineer at Pearster