Lead / Architect Machine Learning

Job Summary:

• We are seeking a highly skilled and versatile Senior AIML/GenAI Lead who embodies the rare combination of a strong software engineer, a pragmatic data scientist, and an expert in building robust, scalable ML applications
• Experience in Gen AI, LLM, ML/DL/NLP, RAG, Lang chain, Mistral, Llama, Hugging Face, Python, Tensorflow, Pytorch, Django, Vector DBThis role is critical to our mission, bridging the gap between cutting-edge ML research and robust, production-ready systems
• You will be instrumental in designing, developing, deploying, and maintaining our core AI-powered products and features
• This demands a blend of analytical rigor, architectural foresight, and a deep understanding of the entire machine learning lifecycle, from data exploration and model development to deployment, monitoring, and continuous improvement
• If you thrive on taking ML models from concept to customer impact and possess exceptional software design skills, we encourage you to apply.

Key Responsibilities:

• ML Model Development & Optimization: Experience in developing and implementing generative AI models and algorithms
• Collaborate with Data Scientists to understand business problems, explore data, develop, train, and evaluate machine learning models (e.g, supervised, unsupervised, deep learning, reinforcement learning)
• Optimize models for performance, efficiency, and interpretabilityEnd-to-End ML Application Development: Lead the design, development, and deployment of machine learning models and intelligent systems into production environments, ensuring they are robust, scalable, and performantSoftware Design & Architecture: Apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services
• Contribute significantly to the architectural decisions for our ML platform and applications
• Data Engineering for ML: Design and implement data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference
• Performance & Scalability: Identify and resolve performance bottlenecks in ML systems
• Ensure the scalability and reliability of deployed models under varying load conditions
• Collaboration & Mentorship: Work closely with cross-functional teams including Data Scientists, Software Engineers, Product Managers, and DevOps to integrate ML solutions seamlessly into our products
• Potentially mentor junior engineers on best practices in ML engineering and software design
• Research & Innovation: Stay abreast of the latest advancements in machine learning, MLOps, and related technologies
• Propose and experiment with new techniques and tools to improve our ML capabilities
• Documentation: Create clear and comprehensive documentation for ML models, pipelines, and services.

Required Qualifications

• Education: Masters degree in computer science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field
• Experience: 10+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role
• Good to have Programming Skills: Proficiency in Python, including experience with writing production-grade, clean, efficient, and well-documented code
• Experience with other languages (e.g, Java, Go, C++) is a plus
• Strong Software Engineering Fundamentals: Deep understanding of software design patterns, data structures, algorithms, object-oriented programming, and distributed systems
• Must have Machine Learning Expertise:o Solid theoretical and practical understanding of various machine learning algorithmso Proficiency with ML frameworks such as PyTorch, Scikit-learn
• o Experience with feature engineering, model evaluation metrics, and hyperparameter tuning
• Data Handling: Experience with SQL and NoSQL databases, data warehousing concepts, and processing large datasets
• Problem-Solving: Excellent analytical and problem-solving skills, with a pragmatic approach to delivering solutions
• Communication: Strong verbal and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.

Preferred Qualifications:

• Master's or Ph.D. in a relevant field.
• Contributions to open-source projects or a strong portfolio of personal projects.
• Experience with A/B testing and experimental design for ML models.
• Knowledge of data governance, privacy, and security best practices in ML.