AIML Engineer

AI / ML Engineer (Generative AI & Agentic AI)

Experience: 3 -5 Years

We’re looking for a highly skilled AI/ML Engineer to design, develop, and deploy advanced machine learning solutions that solve complex business challenges. You’ll work closely with cross-functional teams—Data Engineers, Product Managers, and Software Developers—to bring AI-powered features and insights into production-ready systems.

Key Responsibilities

• Design & Develop ML Models: Build, train, and optimize machine learning and deep learning models for real-world applications (e.g., NLP, computer vision, recommendation systems).
• Data Preparation & Feature Engineering: Collect, clean, and preprocess large-scale datasets for model development.
• Deployment & Integration: Package models into APIs or services, deploy to cloud platforms (AWS, Azure, GCP), and ensure scalability and reliability.
• Monitor & Maintain Models: Track model performance in production, retrain as needed, and manage version control.
• Collaboration: Partner with product and engineering teams to translate business requirements into technical solutions.
• Innovation: Research and implement cutting-edge ML algorithms and tools to improve existing processes and products.
• Transformers, Agentic AI, RAG, Advance RAG, Deep learning basics, Python

Required Skills & Experience

• 3 -5 Years of experience in AI/ML development and deployment.
• Proficiency in Python (pandas, NumPy, scikit-learn) and at least one deep learning framework (TensorFlow or PyTorch).
• Strong understanding of algorithms, statistics, and machine learning techniques (e.g., regression, classification, clustering, CNNs, RNNs).
• Experience with cloud platforms (AWS SageMaker, Azure ML, or GCP AI Platform).
• Familiarity with data engineering concepts (ETL, data pipelines, SQL/NoSQL databases).
• Hands-on experience deploying ML models in production environments (e.g., REST APIs, Docker, Kubernetes).
• Solid problem-solving skills and the ability to work in an agile, fast-paced environment.

Preferred Qualifications

• Exposure to MLOps practices (CI/CD for ML, model monitoring, automated retraining).
• Experience with natural language processing (NLP) or computer vision projects.
• Knowledge of big data tools like Spark or Hadoop.
• Master’s degree in Computer Science, Data Science, or a related field.