Generative AI Engineer

About the Role

We are seeking an experienced Generative AI Engineer

with a minimum of 5 years of professional experience in software engineering, machine learning, artificial intelligence, or a closely related technical discipline

to design, develop, and deploy intelligent AI-powered applications within an enterprise technology environment.

This position offers an opportunity to work on advanced solutions involving Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI Agents, Natural Language Processing (NLP), and intelligent workflow automation

.

The Generative AI Engineer will collaborate with Machine Learning Engineers, Data Scientists, Software Engineers, Cloud Architects, Product Managers, and Information Security teams to develop scalable, reliable, and secure AI applications that support enterprise business operations and digital innovation.

The ideal candidate combines strong proficiency in Python, LLM application development, prompt engineering, model evaluation, vector databases, API integration, and cloud-based AI infrastructure

with practical experience building production-ready software systems.

Essential Duties and Responsibilities

• Design, develop, test, deploy, and maintain enterprise-grade Generative AI applications and services.

• Build and optimize LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and intelligent AI agents

  .

• Integrate foundation models and LLM APIs into scalable software applications and enterprise workflows.

• Develop AI orchestration solutions using LangChain, LangGraph, LlamaIndex

  , or comparable frameworks.

• Implement document ingestion, semantic search, embeddings, and vector retrieval systems.
• Design prompt templates, structured outputs, tool-calling workflows, and context-management strategies.
• Evaluate and improve AI application quality using automated testing, benchmark datasets, and human feedback.
• Implement safeguards addressing hallucinations, prompt injection, sensitive information exposure, and model misuse.

• Develop backend services and APIs using Python, FastAPI

  , or comparable technologies.

• Support model selection, fine-tuning, inference optimization, and performance evaluation where appropriate.
• Build scalable AI deployment workflows using cloud infrastructure, containers, and CI/CD practices.
• Monitor production AI systems for accuracy, latency, cost, reliability, and operational performance.
• Collaborate with Data Engineering and Security teams to maintain secure access to enterprise data sources.
• Participate in architecture reviews, technical design discussions, code reviews, and engineering documentation.
• Stay informed about emerging Generative AI technologies and evaluate their practical business applications.

Job Qualifications and Requirements

• Minimum 5 years of professional experience in Software Engineering, Machine Learning Engineering, Artificial Intelligence, or a closely related technical discipline required.
• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related field, or equivalent practical experience.

• Strong proficiency in Python

  and modern software engineering practices.

• Hands-on experience developing applications using Large Language Models, Generative AI frameworks, or foundation model APIs

  .

• Understanding of transformer architectures, embeddings, tokenization, attention mechanisms, and LLM inference

  .

• Experience building Retrieval-Augmented Generation (RAG)

  solutions.

• Familiarity with LangChain, LangGraph, LlamaIndex, Hugging Face Transformers

  , or comparable technologies.

• Experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, or pgvector

  .

• Knowledge of prompt engineering, function calling, structured generation, and LLM evaluation methodologies

  .

• Experience developing REST APIs and backend services.

• Familiarity with PyTorch, TensorFlow

  , or other machine learning frameworks.

• Experience with AWS, Microsoft Azure, or Google Cloud Platform (GCP)

  .

• Understanding of Docker, Kubernetes, CI/CD, observability, and cloud-native application deployment

  .

• Knowledge of AI security, data privacy, responsible AI, and model governance principles.
• Experience with **fine-tuning, LoRA/PEFT,…