Bachelors degree in Computer Science, Business, or a related field.
Specialization or certification in AI/ML is a plus.
Job Role & Responsibilities :
Develop and optimize predictive models for AI and ML-based features, focusing on enhancing accuracy and latency .
Write clean, efficient, reusable, testable, and scalable code with a focus on best coding practices .
Analyze business requirements, translate them into software components, and implement feature modifications.
Design and implement high-availability, low-latency applications with data protection and security features.
Profile applications to ensure optimal performance and identify potential bottlenecks.
Continuously work on optimizing NLP-based models , improving both their accuracy and latency for real-world applications.
Write unit test cases to ensure code quality and reliability, using appropriate testing frameworks.
Continuously optimize and refactor code for improved performance, scalability, and maintainability.
Skills & Expertise :
Strong expertise in building solutions using AI/ML/DL open-source libraries.
Advanced Python programming skills.
Strong problem-solving and analytical abilities.
Ability to write optimized, well-documented code following best coding practices .
Proficient in optimizing models for both accuracy and latency , particularly in NLP and machine learning applications.
Proficient in optimizing NLP-based models , including techniques for faster inference and reduced computational cost.
Familiarity with Generative AI, Large Language Models (LLM), Embeddings, Vectors, RAG (Retrieval-Augmented Generation), and Prompting.
Tools & Technologies :
AI/ML Libraries : TensorFlow, PyTorch, Flair, BERT, DeBERTa, and other latest libraries for text analytics.
Frameworks & Platforms : Streamlit, FastAPI.
Specialized Tools : Ollama, Vector Databases.
Profiling & Testing : Familiarity with tools for profiling applications (e.g., cProfile, Py-Spy) and writing unit tests (e.g., PyTest, UnitTest).
Optimization Techniques : Techniques such as model quantization, pruning, distillation, and hardware acceleration.
Technical Expertise :
Minimum of 1 year of hands-on experience in AI/ML/DL projects, focusing on Natural Language Processing (NLP) , Named Entity Recognition (NER) , and Text Analytics .
Strong understanding and practical experience with deep learning techniques , including recommendation engines and advanced AI solutions.
Proven experience in optimizing NLP models for accuracy and latency in production environments.
Experience with Retrieval-Augmented Generation (RAG) application development and generative AI.
Familiarity with Agent Prompting and its applications.
Experience in AI applications within the HR domain is highly preferred.
Knowledge of Reinforcement Learning is a plus.
Certifications or specialization in Artificial Intelligence is highly desirable.