Senior AI/ML Engineer

Key Responsibilities

• Lead and Foster a Collaborative Team Environment: Lead, guide, and mentor a team of AI/ML engineers and data scientists to foster a collaborative, innovative, and productive work environment.
• Design and Deploy Machine Learning Systems: Write clean, maintainable, and error-free Python code for developing robust machine learning systems that meet high performance standards.
• Efficient Model Engineering: Design, build, and deploy machine learning models and end-to-end solutions efficiently, leveraging best practices in software development.
• Cross-Functional Collaboration: Work closely with diverse technical teams to ensure timely and efficient delivery of AI/ML projects, promoting open communication and seamless integration.
• End-to-End Data Science Processes: Implement end-to-end data science processes, including data preprocessing, exploratory analysis, modeling, evaluation, and deployment, ensuring rigorous statistical analysis.
• Database Management and Retrieval: Work proficiently with SQL and NoSQL databases to manage and retrieve data effectively, adhering to industry standards.
• API Development and Integration: Develop and integrate RESTful APIs using Python web frameworks (e.g., Flask, Django) to facilitate seamless data exchange between systems.
• Debugging and Optimization: Diagnose issues and optimize existing code for performance improvements, utilizing logical reasoning and hands-on approach.
• Agile Methodologies: Follow Agile/Scrum software development lifecycle practices consistently, prioritizing flexibility and adaptability in project management.
• AI Model Development and Validation: Design, develop, fine-tune, and deploy AI models focusing on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), conducting training, validation, and evaluation experiments to refine models based on statistical analysis and performance metrics.
• Security and Compliance: Ensure that AI/ML solutions adhere to security best practices and data privacy regulations (e.g., GDPR), maintaining confidentiality and integrity of sensitive information.

• Python Expertise: 5 years of hands-on experience with Python programming, demonstrating expertise in coding standards and best practices.
• Machine Learning/DL Experience: 5 years of experience building, training, and deploying Machine Learning and Deep Learning models, showcasing proficiency in algorithm design and implementation.
• Deep Learning Frameworks: Proficiency with TensorFlow, PyTorch, and Keras, highlighting knowledge of neural network architectures and optimization techniques.
• Web Frameworks: Solid experience with Python web frameworks (Flask, Django) for API development, emphasizing expertise in server-side programming and data exchange protocols.
• Core Libraries: Strong expertise in NumPy, Pandas, Matplotlib, Scikit-learn, SpaCy, NLTK, and HuggingFace Transformers, demonstrating mastery of data processing and machine learning libraries.
• Algorithms and Techniques: Deep understanding of ML/DL algorithms, with advanced techniques in NLP and/or Computer Vision, highlighting ability to apply theoretical concepts to real-world problems.
• Production-Grade AI: Proven experience designing and deploying AI solutions leveraging LLMs and RAG, showcasing expertise in model development, deployment, and maintenance.
• NLP and Conversational AI: Hands-on expertise in text representation, semantic extraction, sentiment analysis, and conversational AI, emphasizing knowledge of natural language processing techniques.
• Concurrency: Practical knowledge of multithreading, concurrency, and asynchronous programming in Python, highlighting ability to write efficient and scalable code.
• Database Proficiency: Advanced skills in SQL (PostgreSQL, MySQL, SQLite, MariaDB) and NoSQL (MongoDB) databases, emphasizing expertise in data modeling and query optimization.
• Deployment and MLOps: Experience with containerization (Docker/Kubernetes), cloud platforms (AWS, Azure, GCP), and familiarity with MLOps principles for CI/CD, monitoring, and logging, highlighting ability to deploy and manage AI models in production environments.
• Version Control: Proficient understanding of Git and Bitbucket for software version control, emphasizing knowledge of source code management and collaboration tools.
• C++: Experience with C++ development is a plus, highlighting ability to write efficient and optimized code in multiple programming languages.

• Analytical Thinking: Strong analytical and structured problem-solving capabilities, emphasizing ability to break down complex problems into manageable parts.
• Proactive Problem-Solving: Logical reasoning and a hands-on approach to debugging and optimization, highlighting ability to identify and resolve issues efficiently.
• Team Collaboration: Effective interpersonal skills to work seamlessly with cross-functional teams, emphasizing ability to communicate and collaborate with diverse stakeholders.
• Clear Communication: Excellent ability to articulate technical concepts to diverse audiences, highlighting ability to explain complex ideas simply and effectively.
• Continuous Learning: Commitment to staying current on emerging trends in AI/ML, NLP, and Computer Vision, emphasizing desire to learn and adapt in a rapidly evolving field.