AI-ML Engineer

Role Summary

As an AI/ML Engineer at EVEOAI, you will design, develop, and productionize intelligent systems powering our AI Core SaaS platform. You will work across the complete AI lifecycle—from data collection, preprocessing, and mathematical modeling to Machine Learning, Deep Learning, Generative AI, model deployment, monitoring, and continuous improvement.

You will build scalable AI solutions using structured, unstructured, and multimodal data, including text, images, documents, user interactions, and application logs. Your work will directly contribute to AI accuracy, automation, personalization, product intelligence, and business outcomes.

Core 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.

Requirements

• 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.

what you will be learn beyond the works

• 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.