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.

AIML Engineer

AIML Engineer