AI/ML Engineer

Job description

**Job Title: AI/ML Engineer (Azure/AWS/GCP)** **Experience:** 3–15 Years **Location** : Pune, Mumbai, Chennai, Bangalore, Hyderabad, Kolkata and Noida **Job Summary** We are seeking a highly skilled **AI/ML Engineer** with 3–15 years of experience in designing, developing, deploying, and maintaining Machine Learning and Artificial Intelligence solutions on cloud platforms such as **Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP)** . The ideal candidate should have expertise in machine learning, deep learning, data engineering, MLOps, Generative AI, and cloud-native AI services. The role involves building scalable AI solutions, collaborating with cross-functional teams, and delivering innovative AI-powered applications aligned with business objectives. **Roles & Responsibilities** - Gather, analyze, and understand business requirements to design AI/ML solutions that address complex business problems. - Design, develop, train, validate, and deploy Machine Learning and Deep Learning models for structured and unstructured data. - Build scalable AI solutions using cloud platforms such as Azure, AWS, or GCP. - Develop end-to-end ML pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring. - Implement supervised, unsupervised, reinforcement learning, and deep learning algorithms based on business use cases. - Build and deploy Generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and AI orchestration frameworks. - Fine-tune foundation models and optimize model performance for accuracy, scalability, and cost efficiency. - Develop REST APIs and microservices to expose AI/ML models for enterprise applications. - Collaborate with data engineers, software developers, product owners, and business stakeholders to deliver AI-driven solutions. - Work with structured, semi-structured, and unstructured datasets, ensuring data quality and governance. - Implement MLOps best practices, including CI/CD pipelines, model versioning, monitoring, retraining, and lifecycle management. - Optimize AI models for cloud deployment, performance, latency, scalability, and reliability. - Perform model evaluation, A/B testing, bias detection, explainability, and continuous performance monitoring. - Integrate AI/ML solutions with enterprise applications, databases, APIs, and cloud-native services. - Develop technical documentation, architecture diagrams, and knowledge-sharing materials. - Participate in code reviews, design discussions, and technical solution planning. - Troubleshoot production issues, perform root cause analysis, and provide ongoing production support. - Mentor junior engineers and contribute to AI/ML best practices, reusable frameworks, and innovation initiatives. - Stay updated with emerging AI technologies, cloud AI services, Generative AI advancements, and industry trends. **Key Skills** **Artificial Intelligence & Machine Learning** - Machine Learning - Deep Learning - Natural Language Processing (NLP) - Computer Vision - Predictive Analytics - Recommendation Systems - Time Series Forecasting - Reinforcement Learning - Feature Engineering - Model Evaluation & Optimization - Explainable AI (XAI) **Generative AI** - Large Language Models (LLMs) - Generative AI - Prompt Engineering - Retrieval-Augmented Generation (RAG) - AI Agents - LangChain - LlamaIndex - Vector Databases - Embeddings - Fine-Tuning Foundation Models - AI Model Orchestration **Programming Languages** - Python - SQL - PySpark - R (Preferred) - Java/Scala (Preferred) **Machine Learning Frameworks** - TensorFlow - PyTorch - Scikit-learn - Keras - XGBoost - LightGBM - Hugging Face Transformers **Cloud Platforms** - Microsoft Azure (Azure Machine Learning, Azure OpenAI, Azure AI Services) - Amazon Web Services (AWS SageMaker, Bedrock, Rekognition, Comprehend) - Google Cloud Platform (Vertex AI, AI Platform, BigQuery ML) **MLOps & DevOps** - MLflow - Kubeflow - Docker - Kubernetes - CI/CD Pipelines - Git - GitHub Actions - Jenkins - Model Monitoring - Model Versioning **Soft Skills** - Strong analytical and problem-solving skills - Excellent communication and stakeholder management - Client-facing consulting experience - Leadership and mentoring capabilities - Cross-functional collaboration - Strong presentation and documentation skills - Ability to work in Agile and DevOps environments **Preferred Qualifications** - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Information Technology, Engineering, Mathematics, or a related field. - Certifications in **Microsoft Azure AI Engineer Associate** , **AWS Certified Machine Learning – Specialty** , **Google Professional Machine Learning Engineer** , or equivalent cloud certifications are preferred. - Experience in end-to-end AI/ML solution development, cloud deployment, MLOps, and Generative AI implementations. - Knowledge of Responsible AI, AI Governance, model security, privacy, and ethical AI practices. - Experience working with enterprise-scale AI solutions across industries such as Banking, Healthcare, Retail, Manufacturing, Telecommunications, or Insurance is an added advantage.