Generative AI Engineer Overview We are seeking a visionary GenAI Engineer to lead the design and development of next-generation AI applications using advanced AI services. The ideal candidate will bring deep expertise in GenAI technologies, including Retrieval-Augmented Generation (RAG), intelligent agents, and model fine-tuning, coupled with hands-on experience using tools like LangChain, Model Context Protocol, and Azures full AI stack. Task and Responsibilities: Implement end-to-end Generative AI solutions on the Azure Cloud Platform, leveraging services such as: Azure OpenAI Service, Azure AI Search, Azure Cognitive Services, Azure AI Foundry, Azure Machine Learning etc. Creating data pipelines to ingest, process, and prepare data for analysis and modeling using Azure services, such as Azure AI Document Intelligence , Azure Databricks etc. Design and deploy RAG pipelines, incorporating structured and unstructured data to enhance LLM-based applications. Develop and orchestrate AI Agents and Autonomous Agents for real-time decision-making, task and automation. Fine-tune and optimize Small Language Models ( SLMs ) for domain-specific applications with a focus on performance, cost, and inference latency. Integrate LLMs/SLMs into applications using frameworks such as LangChain , Model Context Protocol ( MCP ), etc. Collaborate with data scientists, ML engineers, and product teams to align AI capabilities with business goals. Lead technical discussions and create architecture blueprints, best practices, and governance models for GenAI adoption across the enterprise. Monitor and optimize deployed solutions for scalability, latency, security, and compliance. Qualifications: Bachelor's or master's degree in computer science, Data Science, AI/ML, information technology, or a related field. Overall 8+ years’ combined experience in IT, 5+ years of experience in AI/ML solution engineering with at least 2+ years focused on Generative AI. Strong experience with LLMs and SLMs, including integration of open source models, prompt engineering, evaluation, and fine-tuning techniques. Proven track record using LangChain, Semantic Kernel, RAG, AI Agents, and MCP (Model Context Protocol) in production environments. Solid understanding and Hands-on experience with vector databases, embeddings, and Azure AI Search integration for semantic retrieval. Hands-on experience with tools like MLFlow, Azure Machine Learning, Azure OpenAI Service, Azure AI Search, Azure Cognitive Services, Azure AI Foundry, Azure Machine Learning etc. Familiarity with MLOps, CI/CD pipelines, and responsible AI practices. Experience deploying AI models on Azure Kubernetes Services (AKS) or Azure Container Apps. Understanding of security and compliance in AI deployments (e.g., data privacy, bias detection). Proficiency in programming languages like Python (essential). Excellent problem-solving, analytical, and critical thinking skills. Strong communication and collaboration skills to work effectively in a team environment. A passion for innovation and a desire to push the boundaries of what's possible with Gen AI. Relevant industry certifications, such as Microsoft Certified: Azure AI Engineer, Azure Solution Architect etc. is a plus.

Job Title: Python AI/ML Engineer Location: Gurugram Experience: 5 to 8 years Job Summary: We are looking for a passionate and skilled Python AI/ML Engineer who brings strong expertise in building AI/ML solutions, productionizing models, and contributing to end-to-end ML pipelines. The ideal candidate should possess deep knowledge of traditional and deep learning concepts, hands-on programming capabilities, experience in enterprise-grade software engineering, and a good understanding of MLOps practices. Key Responsibilities: Design, develop, and deploy scalable machine learning models for classification, regression, NLP, and generative tasks. Build and optimize data transformation workflows using Python and Pandas. Lead AI/ML project pipelines from data ingestion to model deployment and monitoring. Implement model observability, monitoring for drift, and continuous model evaluation. Develop REST APIs and integrate ML models with production systems using frameworks like FastAPI. Participate in code reviews, write unit/integration tests, and ensure high code quality. Collaborate with cross-functional teams including Data Engineers, DevOps, and Product Managers. Stay current with the latest developments in AI, GenAI, ML frameworks, and tools. Use DevOps/MLOps tools to automate and manage model lifecycle processes. Required Skills: Programming & Python Ecosystem: Advanced proficiency in Python, including libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch. Strong understanding of asynchronous programming, FastAPI, and concurrency (Starlette). Deep understanding of multithreading, multiprocessing, and the Python GIL. Ability to write clean, efficient, and testable code. Machine Learning & Deep Learning: Solid grasp of traditional ML concepts: classification, regression, overfitting/underfitting, regularization (L1/L2), multicollinearity. Experience with deep learning: RNNs, attention mechanisms, dropout, early stopping, loss functions (BCE, categorical cross entropy), diffusion models vs GANs. Familiarity with transfer learning and pre-trained model fine-tuning. MLOps: Understanding of ML pipeline design including model training, deployment, and monitoring. Experience in detecting and mitigating data drift and concept drift. Exposure to model observability, monitoring unstructured data drift, and automated drift alerts. Software Engineering & DevOps: Strong skills in REST API development, integration testing, and CI/CD practices. Experience with containerization tools like Docker. Familiarity with cloud-based ML deployment (AWS/Azure) and logging frameworks. Hands-On Problem Solving & Data Engineering: Ability to perform data transformation and aggregation using Python/Pandas. Experience handling dataframes, joins, ranking, filtering, mapping, and custom logic for preprocessing tasks. Nice-to-Have: Experience in GenAI and working with LLMs. Exposure to tools like MLflow, Kubeflow, Airflow, or similar MLOps platforms. Understanding of NLP, embeddings, and transformer-based models. Prior contributions to open-source ML tools or GitHub repositories.