Job description
This role is for one of the Weekday's clients Salary range: Rs 5000000 - Rs 6000000 (ie INR 50-60 LPA) Experience: 8+ yrs Location: Bengaluru Job Type: full-time We are seeking an experienced Lead AI Engineer / Lead Data Scientist to lead the design, development, deployment, and optimization of enterprise-scale AI and Machine Learning solutions.
This role combines deep technical expertise with leadership responsibilities, enabling the successful delivery of advanced analytics, predictive modeling, and AI-driven products that create measurable business impact.
You will work closely with product, engineering, data, and business teams to transform complex business challenges into scalable AI solutions.
The ideal candidate is passionate about building production-grade machine learning systems, driving AI innovation, and establishing best practices across the AI lifecycle, from experimentation to deployment and monitoring.
Key Responsibilities Lead the end-to-end development of Machine Learning and AI solutions, including model design, training, validation, deployment, and monitoring.
Build scalable and reusable ML pipelines for data preparation, feature engineering, model training, evaluation, and production deployment.
Translate business requirements into effective AI strategies, predictive models, and deployment roadmaps.
Design and implement advanced analytics solutions such as forecasting, recommendation systems, anomaly detection, classification models, and optimization engines.
Collaborate with Data Engineers and Software Engineers to integrate AI models into business applications, APIs, and operational workflows.
Establish and manage MLOps practices, including experiment tracking, model versioning, CI/CD pipelines, automated deployments, monitoring, drift detection, retraining, and rollback strategies.
Ensure model reliability, scalability, explainability, and governance while maintaining high standards for quality and compliance.
Monitor production systems for performance, accuracy, latency, and business outcomes, driving continuous improvement initiatives.
Mentor and guide Data Scientists and ML Engineers, promoting technical excellence and knowledge sharing.
Contribute to AI platform architecture decisions and help define enterprise-wide standards for machine learning development and deployment.
Drive innovation through the adoption of emerging AI technologies, including Generative AI, LLMs, Retrieval-Augmented Generation (RAG), and advanced analytics frameworks.
What Makes You a Great Fit 8+ years of experience in Data Science, Machine Learning, Applied AI, or Advanced Analytics with proven success in delivering production-grade AI solutions.
Strong expertise in machine learning algorithms, feature engineering, model evaluation, tuning, and performance optimization.
Advanced programming skills in Python and hands-on experience with frameworks such as Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, PyTorch, and TensorFlow.
Extensive experience building and managing end-to-end ML pipelines and scalable AI applications.
Strong understanding of MLOps practices, model lifecycle management, CI/CD automation, and cloud-native AI deployment.
Experience with cloud platforms, containerization technologies, orchestration tools, and modern DevOps workflows.
Deep knowledge of SQL, APIs, feature stores, model registries, monitoring frameworks, and data engineering concepts.
Familiarity with Large Language Models (LLMs), Generative AI, embeddings, RAG architectures, and AI-powered business applications.
Strong analytical thinking, problem-solving capabilities, and ability to influence technical and business stakeholders.
Proven leadership experience mentoring teams, driving AI initiatives, and delivering measurable business outcomes.
Excellent communication, stakeholder management, and cross-functional collaboration skills.
Ability to thrive in fast-paced environments while balancing strategic planning with hands-on technical execution.
This job post has been translated by AI and may contain minor differences or errors.
