Generative Ai Engineer

We are seeking a highly experienced and innovative First Responder AI Expert with 3-8 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. The ideal candidate will be responsible for creating, deploying, and optimizing AI agents that act as a "first responder" to various queries and eventsfrom 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.

Key Responsibilities:

End-to-End Agent Development:

Design, develop, and deploy conversational and autonomous AI agents that can triage, answer, and resolve user requests in real-time. Leverage and fine-tune large language models (LLMs) and other generative AI technologies to build highly effective "first responder" applications. Implement robust RAG (Retrieval Augmented Generation) systems to ground agent responses in accurate, up-to-date, and proprietary organizational data. Workflow Automation: 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. Design and implement agentic workflows that can handle multi-step processes and hand-off tasks to human operators seamlessly when necessary. Prompt Engineering & Agent Design: Develop and optimize sophisticated prompt engineering strategies to ensure agents provide accurate, relevant, and contextually appropriate responses. Design agent personas and interaction models that align with business needs and provide a positive user experience. Data & Knowledge Management: Collaborate with data and content owners to identify, ingest, and manage knowledge bases and data sources essential for agent functionality. Implement strategies for continuous learning and retraining of agents based on user interactions and new data.

Performance Monitoring & Optimization: Establish key performance indicators (KPIs) for agent success, such as resolution rate, response time, and user satisfaction. Develop monitoring systems to track agent performance, identify areas for improvement, and mitigate potential issues like "hallucinations" or biased responses. Collaboration & Leadership: Act as a subject matter expert on "first responder" AI solutions, providing technical guidance to cross-functional teams. Work closely with product managers and business stakeholders to identify high-impact use cases and define agent capabilities. Mentor and guide junior engineers in the best practices of agent development.

Required Qualifications: Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

Experience: 3-8 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. Programming: Strong proficiency in Python is non-negotiable.

NLP & Generative AI Expertise: Deep, hands-on experience with NLP techniques, including intent recognition, entity extraction, and sentiment analysis.

Extensive experience with Large Language Models (LLMs) and fine-tuning using frameworks like Hugging Face Transformers.

Practical experience in designing and implementing RAG (Retrieval Augmented Generation) systems.

Proficiency in prompt engineering and understanding of various prompt templates and strategies.

MLOps & Cloud: Experience with deploying and managing AI agents in a production environment using MLOps practices.

Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and related services for AI/ML and serverless computing.

Familiarity with containerization (Docker) and orchestration (Kubernetes). System Integration:

Proven experience integrating AI solutions with external APIs and internal enterprise systems (e.g., Salesforce, Jira, Slack). Problem-Solving