AI Developer

Job Overview

Our client is looking for an AI Developer with hands-on experience in developing and deploying enterprise AI solutions powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Machine Learning (ML). This role is responsible for building intelligent applications that enhance automation, operational efficiency, and incident analysis while integrating seamlessly with enterprise platforms.

The successful candidate will design end-to-end AI workflows, implement vector search solutions, optimize retrieval pipelines, and develop machine learning models for anomaly detection. The role also requires a strong understanding of AI governance, model lifecycle management, and Responsible AI practices to ensure scalable, secure, and production-ready solutions.

Key Responsibilities

• Design and develop AI-powered solutions using Large Language Models (LLMs) to support automation, root cause analysis, incident summarization, and intelligent workflow orchestration.
• Build machine learning models that identify anomalies and patterns within operational and infrastructure data.
• Integrate AI capabilities into existing enterprise systems to improve monitoring, reporting, and business process automation.
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embedding generation, and semantic retrieval.
• Configure and maintain vector databases to support high-performance similarity search and contextual information retrieval.
• Collaborate with cross-functional stakeholders to gather requirements, validate AI solutions, and continuously improve model performance.
• Ensure AI applications comply with Responsible AI principles, governance standards, and documentation requirements.
• Support the deployment, monitoring, and ongoing optimization of AI and machine learning models in production environments.
  
  
  

• Demonstrated experience developing and deploying AI-powered applications in production environments.
• Hands-on experience building transformer-based workflows, prompt engineering solutions, and LLM-driven automation.
• Experience implementing Retrieval-Augmented Generation (RAG) architectures using modern retrieval and context management techniques.
• Practical experience with vector databases and semantic search technologies, including indexing, metadata filtering, and data partitioning strategies.
• Experience integrating AI solutions with enterprise applications and operational platforms.
• Knowledge of the complete machine learning lifecycle, including model development, evaluation, deployment, and production monitoring.
  
  
  

• Large Language Models (LLMs) and Generative AI technologies.
• Transformer architectures, embeddings, prompt engineering, and model optimization techniques.
• Retrieval pipelines, vector indexing, and semantic search methodologies.
• Retrieval-Augmented Generation (RAG) architectures and context optimization strategies.
• Vector databases, similarity search, metadata filtering, and indexing techniques.
• Embedding generation, document chunking strategies, and retrieval frameworks such as LangChain, LlamaIndex, or similar technologies.
  
  
  

• Machine learning concepts, including supervised, unsupervised, and deep learning approaches.
• Feature engineering and data preparation using Python-based machine learning libraries.
• Time-series forecasting techniques and predictive modeling.
• Supervised learning algorithms for anomaly detection and predictive analytics.
• Model deployment using containerization, orchestration platforms, and MLOps tools.
• Monitoring model performance, drift detection, observability, and automated AI/ML pipelines.
  
  
  

• Strong analytical and problem-solving skills with the ability to develop practical AI solutions for complex business challenges.
• Proficiency in Python and modern AI/ML development frameworks.
• Ability to design scalable, production-ready AI architectures.
• Experience working collaboratively within cross-functional engineering and product teams.
• Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
• Commitment to AI governance, security, and Responsible AI best practices.