Overview

Disclaimer: This job posting has been aggregated from external source

Responsibilities

• Design, develop, and deploy scalable AI/ML models and intelligent applications for enterprise use cases.
• Lead the development of Generative AI and Agentic AI solutions, including AI agents, LLM-powered applications, and multi-agent workflows.
• Develop and optimize machine learning and deep learning models for classification, prediction, natural language processing, and other relevant use cases.
• Work with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering.
• Design and implement AI pipelines, data preprocessing workflows, model evaluation frameworks, and inference services.
• Build and integrate AI solutions using Python, REST APIs, and relevant AI/ML frameworks.
• Evaluate and implement suitable models, libraries, and AI platforms based on business requirements.
• Optimize model performance, latency, scalability, reliability, and inference costs.
• Establish best practices for model versioning, deployment, monitoring, testing, and continuous improvement.
• Collaborate with product managers, software engineers, and other stakeholders to translate business requirements into technical solutions.
• Lead technical discussions, conduct code reviews, mentor junior developers, and promote engineering best practices.
• Stay updated on emerging developments in Generative AI, Agentic AI, and machine learning technologies.

Qualifications

• 5+ years of hands-on experience in AI/ML development, with demonstrated experience delivering production-ready solutions.
• Strong proficiency in Python and commonly used libraries such as NumPy, Pandas, Scikit-learn, and relevant deep learning frameworks.
• Strong understanding of machine learning algorithms, deep learning architectures, statistics, and model evaluation techniques.
• Practical experience with Generative AI, LLMs, prompt engineering, embeddings, and RAG-based applications.
• Experience with AI agent frameworks and orchestration tools such as LangChain, LangGraph, LlamaIndex, or equivalent technologies.
• Experience with frameworks such as PyTorch or TensorFlow.
• Hands-on experience with vector databases such as FAISS, Pinecone, Weaviate, Chroma, or equivalent solutions.
• Experience building APIs and integrating AI models into enterprise applications.
• Knowledge of Docker, Git, CI/CD, and cloud-based AI/ML deployment environments.
• Experience with model monitoring, performance optimization, and production troubleshooting.
• Strong problem-solving, analytical, communication, and technical decision-making skills.
• Experience in developing and deploying Agentic AI projects or products, including AI agents, multi-agent systems, autonomous workflows, and LLM-powered enterprise applications, will be strongly preferred.
• Experience leading AI/ML projects or mentoring developers.
• Exposure to AWS, Azure, or Google Cloud AI services.
• Experience with MLOps, MLflow, model registries, and automated model deployment pipelines.
• Understanding of AI security, responsible AI practices, data privacy, and LLM evaluation.
• Experience developing enterprise-grade AI products, intelligent automation solutions, or Agentic AI applications.