Job Title

Title: GEN AI and Machine Learning Engineer Experience: 3-8yrs Location: Bangalore , Gurugram Roles and Responsibilities Qualifications: Bachelor's or Master’s degree in Computer Science, Data Science, Engineering, or a related field. Solid foundation in mathematics, statistics, and probability. Strong programming skills in Python and proficiency in SQL/NoSQL databases. Core Skills & Experience: Hands-on experience with Agentic AI frameworks . Proven ability to build analytical approaches from business requirements, and develop, train, and deploy machine learning and AI models. Exposure to Generative AI models and tools such as OpenAI, Google Gemini, Runway ML , etc. Experience with AI/ML and deep learning frameworks including TensorFlow, PyTorch, Scikit-learn, OpenCV, and Keras . Familiarity with a range of ML, NLP/NLU, and deep learning algorithms. Experience developing RESTful APIs using Flask or Django . Proficiency in cloud environments such as AWS, Azure, or GCP . Understanding of Vector Databases , Embeddings , and LLMs (Large Language Models) . Good-to-Have Skills: Experience with MLOps tools such as MLFlow, Kubeflow , and CI/CD pipelines. Familiarity with Docker , Kubernetes , and container orchestration. Exposure to frontend technologies : HTML, CSS, JavaScript/jQuery, Node.js, Angular, or React. Additional experience with Flask/Django is a plus. Key Responsibilities: Collaborate with software engineers, business stakeholders, and domain experts to convert business needs into AI-driven features and solutions. Design, develop, and deploy AI/ML, NLP/NLU , and deep learning models and applications. Preprocess and analyze large-scale datasets to extract meaningful insights and identify trends. Evaluate and optimize model performance to ensure accuracy, scalability, and generalization. Deploy AI/ML applications in cloud environments (AWS, Azure, or GCP). Continuously monitor model performance in production and make necessary updates or improvements. Document development processes, results, and best practices for transparency and continuous learning.