<p>Job Summary :<br/><br/></p><p>We are seeking a highly experienced and innovative First Responder AI Expert with 5-7 years of experience to lead the development of AI agents designed for instant, context-aware responses.
This role is a blend of AI engineering, natural language processing (NLP), and deep domain knowledge of business operations.
</p><p><br/></p><p>The ideal candidate will be responsible for creating, deploying, and optimizing AI agents that act as a "first responder" to various queries and events-from a customer's support request to an internal employee's question about HR policies or a sales team's need for real-time data.
You will be instrumental in automating support, knowledge access, and operational workflows across the organization.<br/><br/></p><p>Key Responsibilities :<br/><br/>End-to-End Agent Development :<br/><br/></p><p>- Design, develop, and deploy conversational and autonomous AI agents that can triage, answer, and resolve user requests in real-time.<br/><br/></p><p>- Leverage and fine-tune large language models (LLMs) and other generative AI technologies to build highly effective "first responder" applications.<br/><br/></p><p>- Implement robust RAG (Retrieval Augmented Generation) systems to ground agent responses in accurate, up-to-date, and proprietary organizational data.<br/><br/></p><p>Workflow Automation :<br/><br/></p><p>- Integrate AI agents with internal business systems (e.g., Slack, Jira, Salesforce, CRM, ERP) using APIs to enable autonomous actions like creating tickets, updating records, or providing status updates.<br/><br/></p><p>- Design and implement agentic workflows that can handle multi-step processes and hand-off tasks to human operators seamlessly when necessary.<br/><br/></p><p>Prompt Engineering & Agent Design :<br/><br/></p><p>- Develop and optimize sophisticated prompt engineering strategies to ensure agents provide accurate, relevant, and contextually appropriate responses.<br/><br/></p><p>- Design agent personas and interaction models that align with business needs and provide a positive user experience.<br/><br/></p><p>Data & Knowledge Management :<br/><br/></p><p>- Collaborate with data and content owners to identify, ingest, and manage knowledge bases and data sources essential for agent functionality.<br/><br/></p><p>- Implement strategies for continuous learning and retraining of agents based on user interactions and new data.<br/><br/></p><p>Performance Monitoring & Optimization :<br/><br/></p><p>- Establish key performance indicators (KPIs) for agent success, such as resolution rate, response time, and user satisfaction.<br/><br/></p><p>- Develop monitoring systems to track agent performance, identify areas for improvement, and mitigate potential issues like "hallucinations" or biased responses.<br/><br/></p><p>Collaboration & Leadership :<br/><br/></p><p>- Act as a subject matter expert on "first responder" AI solutions, providing technical guidance to cross-functional teams.<br/><br/></p><p>- Work closely with product managers and business stakeholders to identify high-impact use cases and define agent capabilities.<br/><br/></p><p>- Mentor and guide junior engineers in the best practices of agent development.<br/><br/></p><p>Required Qualifications :<br/><br/>Education : </p><p><br/></p><p>- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.<br/><br/></p><p>Experience : </p><p><br/></p><p>- 5-7 years of professional experience in an AI Engineer, NLP Engineer, or similar role, with a proven track record of deploying conversational or agentic AI solutions to production.<br/><br/></p><p>Programming : </p><p><br/></p><p>- Strong proficiency in Python is non-negotiable.<br/><br/></p><p>NLP & Generative AI Expertise :<br/><br/></p><p>- Deep, hands-on experience with NLP techniques, including intent recognition, entity extraction, and sentiment analysis.<br/><br/></p><p>- Extensive experience with Large Language Models (LLMs) and fine-tuning using frameworks like Hugging Face Transformers.<br/><br/></p><p>- Practical experience in designing and implementing RAG (Retrieval Augmented Generation) systems.<br/><br/></p><p>- Proficiency in prompt engineering and understanding of various prompt templates and strategies.<br/><br/></p><p>MLOps & Cloud :<br/><br/></p><p>- Experience with deploying and managing AI agents in a production environment using MLOps practices.<br/><br/></p><p>- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and related services for AI/ML and serverless computing.<br/><br/></p><p>- Familiarity with containerization (Docker) and orchestration (Kubernetes).<br/><br/></p><p>System Integration :<br/><br/></p><p>- Proven experience integrating AI solutions with external APIs and internal enterprise systems (e.g., Salesforce, Jira, Slack).<br/><br/></p><p>- Problem-Solving: Exceptional analytical and problem-solving skills, with the ability to design and implement robust, real-time AI solutions for a wide range of use cases.<br/><br/></p><p>Preferred Qualifications (Bonus Points) :<br/><br/>- Experience with specific agent frameworks (e.g., LangChain, LlamaIndex, Google's Agent Development Kit).<br/><br/></p><p>- Prior experience in developing AI solutions for IT support, HR, sales, or customer service.<br/><br/></p><p>- Understanding of ethical AI principles, safety, and bias mitigation in conversational systems.<br/><br/></p><p>- Contributions to open-source AI projects.<br/><br/></p><p>- Experience with real-time streaming data technologies (e.g., Kafka).</p> (ref:hirist.tech)