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.
