AI Engineer

Role Overview

As an AI Engineer at GrowthNXT, you will design, build, and deploy production-ready AI-powered product features spanning LLM-powered assistants, autonomous agentic workflows, recommendation systems, and multimodal intelligence. You will bridge research and engineering to create practical, scalable AI products that solve real business and industrial problems. You will own the end-to-end development of Retrieval-Augmented Generation (RAG) systems, semantic search, intelligent ranking, and knowledge pipelines, while working closely with product and engineering teams to translate business requirements into practical AI solutions.

Responsibilities

• Design, build, and deploy AI-powered product features.
• Build Retrieval-Augmented Generation (RAG) systems and knowledge pipelines.
• Develop multimodal AI applications involving text, vision, and structured data.
• Optimize latency, inference costs, and model performance.
• Collaborate closely with product and engineering teams to translate business requirements into AI solutions.
• Develop agentic workflows using modern LLM orchestration frameworks.
• Implement semantic search, recommendation engines, and intelligent ranking systems.
• Integrate foundation models through APIs and self-hosted deployments.
• Evaluate emerging AI tooling and rapidly prototype new capabilities.

Requirements

• Strong programming skills in Python.
• Solid understanding of machine learning fundamentals.
• Experience working with REST APIs and cloud services.
• Knowledge of Git and modern software development practices.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.

Nice to Have

• Experience with LLMs and agent frameworks such as LangGraph, CrewAI, AutoGen, and OpenAI Agents SDK.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, and Chroma.
• Familiarity with RAG architectures.
• Experience deploying AI models using Docker and Kubernetes.
• Knowledge of prompt engineering and evaluation methodologies.
• Familiarity with MLOps workflows.
• Exposure to computer vision or multimodal AI.
• Understanding of cloud AI platforms including AWS, Azure, and GCP.

Skills

• Python
• LangChain
• RAG
• Vector Databases
• Docker

Benefits

• Work from home