Sr. AI/ML Developer

Key Responsibilities Team Leadership: Lead, guide, and mentor a team of AI/ML engineers and data scientists, fostering a collaborative, innovative, and productive work environment. Clean Code & Development: Write clean, maintainable, and error-free Python code for developing robust machine learning systems. Model Engineering: Efficiently design, build, and deploy machine learning models and end-to-end solutions. Cross-Functional Collaboration: Work closely with diverse technical teams to ensure timely and efficient delivery of AI/ML projects. Data Science Lifecycle: Implement end-to-end data science processes, including data preprocessing, exploratory analysis, modeling, evaluation, and deployment. Database Management: Work proficiently with SQL and NoSQL databases to manage and retrieve data effectively. API Development: Develop and integrate RESTful APIs using Python web frameworks (e.g., Flask, Django). Debugging & Optimization: Diagnose issues and optimize existing code for performance improvements. Agile Methodologies: Follow Agile/Scrum software development lifecycle practices consistently. LLM & RAG Implementation: Design, develop, fine-tune, and deploy AI models focusing on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Model Validation & Evaluation: Conduct training, validation, and evaluation experiments, refining models based on statistical analysis and performance metrics. Security & Compliance: Ensure that AI/ML solutions adhere to security best practices and data privacy regulations (e.g., GDPR). Technical Qualifications Python Expertise: 3–5 years of hands-on experience with Python programming. ML/DL Experience: 3–5 years of experience building, training, and deploying Machine Learning and Deep Learning models. Deep Learning Frameworks: Proficiency with TensorFlow, PyTorch, and Keras. Web Frameworks: Solid experience with Python web frameworks (Flask, Django) for API development. Core Libraries: Strong expertise in NumPy, Pandas, Matplotlib, Scikit-learn, SpaCy, NLTK, and HuggingFace Transformers. Algorithms & Techniques: Deep understanding of ML/DL algorithms, with advanced techniques in NLP and/or Computer Vision. Production-Grade AI: Proven experience designing and deploying AI solutions leveraging LLMs and RAG. NLP & Conversational AI: Hands-on expertise in text representation, semantic extraction, sentiment analysis, and conversational AI. Concurrency: Practical knowledge of multithreading, concurrency, and asynchronous programming in Python. Database Proficiency: Advanced skills in SQL (PostgreSQL, MySQL, SQLite, MariaDB) and NoSQL (MongoDB) databases. Deployment & MLOps: Experience with containerization (Docker/Kubernetes), cloud platforms (AWS, Azure, GCP), and familiarity with MLOps principles for CI/CD, monitoring, and logging. Version Control: Proficient understanding of Git and Bitbucket for software version control. C++: Experience with C++ development is a plus. Soft Skills Analytical Thinking: Strong analytical and structured problem-solving capabilities. Proactive Problem-Solving: Logical reasoning and a hands-on approach to debugging and optimization. Team Collaboration: Effective interpersonal skills to work seamlessly with cross-functional teams. Clear Communication: Excellent ability to articulate technical concepts to diverse audiences. Continuous Learning: Commitment to staying current on emerging trends in AI/ML, NLP, and Computer Vision.