AI Engineer - Generative AI

About the Role:

We are looking for an experienced AI Engineer / Software Engineer to design, develop, and deploy Generative AI solutions, including LLM-powered applications, Retrieval-Augmented Generation (RAG), and agentic workflows.

The role involves integrating AI capabilities with enterprise data and systems to build reliable, scalable, and secure GenAI applications. The candidate will work closely with senior engineers and cross-functional teams, focusing on AI evaluation, observability, safety, and production performance.

Key Responsibilities:

• Design and develop LLM-powered conversational applications, including chat assistants and AI agents.

• Integrate AI agents with tools, APIs, and enterprise data sources.

• Build and maintain RAG pipelines, including document ingestion, chunking, embeddings, indexing, and retrieval.

• Work with vector databases and AI-powered search systems.

• Develop LLM inference and orchestration components, including prompt templates, function/tool calling, streaming, and caching.

• Implement safety and compliance guardrails, including content filtering, prompt injection protection, and data access controls.

• Support security testing and red-teaming of GenAI applications.

• Optimize GenAI solutions for cost, latency, and performance while maintaining response quality.

• Implement Responsible AI practices, including grounding, hallucination mitigation, privacy-by-design, and bias/toxicity evaluation.

• Ensure AI solutions align with security and compliance requirements.

• Monitor and evaluate GenAI application performance through quality and safety metrics, tracing, and cost monitoring.

• Document technical designs, prompts, evaluation results, and operational runbooks.

• Develop reusable components and contribute to engineering best practices.

Required Skills & Experience:

• 3 - 5 years of total software engineering experience, including hands-on experience building and deploying AI/ML and Generative AI solutions.

• Strong programming expertise in Python.

• Practical experience developing LLM-based applications using: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering and Tool Calling, and AI Agents and Agentic Workflows.

• Experience with LLM frameworks such as LangChain and LangGraph.

• Hands-on experience deploying GenAI applications on cloud platforms such as AWS, Azure, or GCP.

• Experience integrating managed LLM services and implementing secure API patterns.