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Niche LLM/agent skillset and smaller employer reduce applicant density.
Specialized LLM and MLOps expertise is transferable across industries but requires specific AI experience.
Extensive mandatory LLM, MLOps, and integration tech stack increases candidate filtering stringency.
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Develop and deploy production AI agent solutions integrating large language models, custom prompts, and business logic across financial, manufacturing, and enterprise domains.
Build and optimize Retrieval-Augmented Generation (RAG) systems using vector indices, embedding models, and prompt orchestration to ensure grounded AI outputs.
Integrate AI agents with external systems and deploy them using containerization and cloud platforms ensuring scalability, monitoring, and compliance with responsible AI guidelines.
Proficient in Python with experience in FastAPI, Flask, RESTful APIs, and related data formats (JSON/XML).
Hands-on experience with AI/ML frameworks including LLM APIs (OpenAI, Azure OpenAI), LangChain, PyTorch or TensorFlow, and vector databases like Pinecone or Weaviate.
Experience deploying applications with Docker on cloud platforms such as AWS, Azure, or GCP and familiarity with event stream technologies (Kafka, RabbitMQ, SQS).
Work Experience Required: Not explicitly mentioned in the JD
Experienced in end-to-end AI agent development involving large language models and multi-agent systems in client-facing roles.
Skilled in merging AI capabilities with enterprise system integrations and deploying scalable solutions in hybrid cloud environments.
Familiar with implementing ethics, content moderation, and data privacy governance following responsible AI practices.
