Overview

As an AI/ML Engineer at EVEOAI, you will design, develop, and productionize intelligent systems powering our AI Core SaaS platform.

Responsibilities

• Develop and manage end-to-end AI/ML pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, inference, monitoring, and retraining.
• Design, train, fine-tune, and optimize Machine Learning and Deep Learning models for real-world SaaS applications.
• Work with multimodal data including text, images, documents, user behavior, and application telemetry.
• Apply mathematical and statistical concepts including Linear Algebra, Probability, Statistics, Calculus, Optimization, Gradient Descent, Loss Functions, Regularization, and Information Theory.
• Build ML solutions using regression, classification, clustering, recommendation, anomaly detection, ranking, and other suitable algorithms.
• Develop Deep Learning solutions using neural networks, CNNs, RNNs/LSTMs, Transformers, attention mechanisms, and representation-learning techniques.
• Build and integrate Generative AI, NLP, LLM, embeddings, semantic search, RAG, and multimodal AI capabilities.
• Perform EDA, feature engineering, experimentation, hyperparameter optimization, model validation, error analysis, and performance tuning.
• Productionize AI models and services using MLflow, Azure ML, Docker, Kubernetes, or similar technologies.
• Build scalable batch and real-time inference pipelines and APIs for integration with EVEOAI's AI Core SaaS platform.
• Monitor model performance, data quality, data drift, concept drift, latency, reliability, and resource utilization.
• Develop automated model evaluation, versioning, deployment, retraining, and continuous improvement workflows.
• Collaborate with Data Engineering, Software Engineering, NLP, Product, and other AI/ML teams to take AI solutions from experimentation to production.
• Research and evaluate emerging AI/ML technologies and integrate relevant techniques into EVEOAI products.

Qualifications

• BTech/MTech/MSc/PhD (or equivalent) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, or a related quantitative field.
• Strong Python programming skills; advanced knowledge of NumPy, Pandas, Scikit-learn, and SQL.
• Strong understanding of Machine Learning algorithms, model evaluation, feature engineering, optimization, and statistical modeling.
• Strong understanding of Deep Learning and practical experience with PyTorch, TensorFlow, or similar frameworks.
• Good mathematical foundation in Linear Algebra, Probability, Statistics, Calculus, Optimization, and Information Theory.
• Experience working with NLP, Transformers, LLMs, Embeddings, RAG, Generative AI, Computer Vision, or multimodal AI.
• Experience building and deploying end-to-end ML/DL pipelines from data preparation through production inference.
• Knowledge of model deployment, experiment tracking, monitoring, and MLOps using MLflow, Airflow, Azure ML, or equivalent tools.
• Experience with Azure ecosystem and services such as Azure ML, Data Factory, Blob Storage, Cosmos DB, or similar cloud platforms.
• Understanding of Git, APIs, Docker, CI/CD, monitoring, and production software engineering practices.
• Advanced experience with big-data technologies such as PySpark and Databricks.
• Experience with real-time streaming technologies such as Kafka.
• Experience with Kubernetes, GPU computing, distributed model training, or cloud-native AI infrastructure.
• Experience with vector databases, semantic search, AI agents, or agentic AI systems.
• Experience with LLM fine-tuning, LoRA/PEFT, model optimization, quantization, or inference optimization.
• Experience building and scaling AI-powered SaaS products or multi-tenant AI platforms.
• Strong participation in AI/ML competitions, research publications, open-source AI projects, or production-grade AI projects.