Description

What You'll Do:

• Design, develop, and deploy AI/ML solutions for enterprise-scale applications and platforms.
• Build production-ready AI systems leveraging Large Language Models (LLMs), Machine Learning, and Generative AI technologies.
• Develop scalable data pipelines and AI workflows to support model training, inference, and continuous improvement.
• Design, train, test, evaluate, and optimize machine learning models for performance, scalability, reliability, and security.
• Implement Retrieval-Augmented Generation (RAG) solutions and intelligent search capabilities.
• Develop prompt engineering strategies and optimize interactions with foundation models.
• Collaborate with business stakeholders, architects, product teams, and engineering teams to translate business requirements into AI-driven solutions.
• Integrate AI services with enterprise applications through APIs, microservices, and cloud-native architectures.
• Deploy, monitor, maintain, and enhance AI applications in production environments.
• Implement MLOps frameworks to automate model training, deployment, versioning, monitoring, and lifecycle management.
• Perform model performance analysis, validation, and continuous optimization.
• Ensure Responsible AI practices, governance, security, and compliance requirements are followed.
• Support troubleshooting, root-cause analysis, and production issue resolution.
• Create and maintain technical documentation, architecture designs, and operational runbooks.
• Stay updated with emerging AI technologies, frameworks, and industry best practices.

Expertise You'll Bring:

• 8-10 years of experience in Artificial Intelligence, Machine Learning, Data Science, or Advanced Analytics.
• Strong hands-on expertise in Python and AI application development.
• Experience building enterprise-grade AI solutions using Large Language Models (LLMs), Machine Learning, and Generative AI technologies.
• Strong knowledge of model development, evaluation, fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG).
• Hands-on experience with AI frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, or similar technologies.
• Strong understanding of Machine Learning algorithms, Deep Learning, Natural Language Processing (NLP), and Generative AI concepts.
• Experience working with SQL and NoSQL databases.
• Expertise in building and managing data pipelines and large-scale data processing workflows.
• Experience with vector databases such as Pinecone, ChromaDB, Weaviate, FAISS, or similar platforms.
• Experience integrating AI services through APIs and microservices-based architectures.
• Hands-on experience with cloud platforms including AWS, Azure, or Google Cloud Platform and their AI/ML services.
• Strong understanding of MLOps practices including model deployment, CI/CD pipelines, monitoring, version control, and lifecycle management.
• Experience with Docker, Kubernetes, containerized deployments, and cloud-native architectures.
• Knowledge of observability, logging, monitoring, performance optimization, scalability, reliability, and AI security practices.
• Strong analytical, problem-solving, debugging, and troubleshooting capabilities.
• Excellent communication, stakeholder management, and collaboration skills

Key Skills

Education

Any Graduate