AI ENGINEER — AGENTIC AI & APPLIED INTELLIGENCE
About the Role
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. The role also involves model integration, latency and inference-cost optimization, evaluation frameworks, and rapid prototyping of emerging AI capabilities.
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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Technology Stack
Python, LangGraph, CrewAI, AutoGen, OpenAI SDK, Pinecone, Weaviate, Qdrant, Chroma, RAG, PyTorch, TensorFlow, Docker, Kubernetes, AWS, GCP, Azure, FastAPI, LangChain, Hugging Face, vLLM, Prompt Engineering, and MLOps. The role focuses on application and experience layers including assistants, agents, and ranking; orchestration and reasoning through agent frameworks and tool use; retrieval and knowledge through vector databases, embeddings, and RAG; model and inference through foundation models, vLLM, and fine-tuning; and infrastructure and MLOps through Docker, Kubernetes, monitoring, and CI/CD.
Ideal Candidate
We are looking for highly motivated, self-driven engineers who take ownership of outcomes, are available to get started right away, stay current with developments in LLMs, agents, and applied AI, and have a strong bias toward shipping production-quality systems rather than focusing solely on research. The ideal candidate can understand product vision and translate it into practical AI solutions, learn new tools and technologies rapidly, work comfortably in a fast-paced cross-functional environment, and communicate effectively with product and engineering teams.
Pay: ₹40,000.00 - ₹45,000.00 per month
Benefits:
Application Question(s):
- Do you have strong programming experience in Python?
Do you have experience working with REST APIs and cloud services?
Do you have practical experience building or working with RAG systems?
Which of the following AI/LLM technologies have you worked with?
Work Location: Remote
