Role & responsibilities

- Lead the design, development, deployment, and maintenance of production-grade machine learning systems.
- Provide technical leadership to ML Engineers, Data Scientists, and AI Engineers across the ML lifecycle.
- Translate business and product requirements into scalable ML solutions and technical architectures.
- Develop and productionize models for prediction, classification, recommendation, NLP, computer vision, and Generative AI use cases.
- Design and implement robust ML pipelines for data preparation, feature engineering, training, validation, deployment, and monitoring.
- Establish and improve MLOps practices, including CI/CD, model versioning, experiment tracking, model serving, and monitoring.
- Optimize models and ML infrastructure for accuracy, latency, scalability, reliability, and cost efficiency.
- Design scalable model-serving and inference architectures for batch and real-time applications.
- Evaluate and integrate LLMs, foundation models, RAG, vector databases, and AI-agent technologies where appropriate.
- Collaborate with Data Engineering, Software Engineering, Product, DevOps, and Data Science teams.
- Conduct technical design reviews, code reviews, architecture reviews, and establish engineering best practices.
- Troubleshoot production ML issues and lead root-cause analysis and resolution.
- Define technical roadmaps, estimate engineering effort, prioritize initiatives, and ensure timely delivery.
- Mentor engineers and promote best practices in ML engineering, software development, testing, and documentation.
- Ensure ML systems comply with requirements for data privacy, security, model governance, fairness, and responsible AI.
- Research emerging ML technologies and identify opportunities to improve existing products and platforms.

Preferred candidate profile





- 5 to 10 years of experience in Machine Learning Engineering, AI Engineering, Data Science, or related technical roles, with demonstrated technical leadership.
- Strong hands-on expertise in Python, Machine Learning, Deep Learning, and production ML systems.
- Strong proficiency in PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, or equivalent ML frameworks.
- Experience with Generative AI, LLMs, RAG, embeddings, vector databases, prompt engineering, fine-tuning, and model evaluation is highly desirable.
- Solid understanding of MLOps, including MLflow/Kubeflow, experiment tracking, model versioning, deployment, monitoring, and CI/CD.
- Experience deploying ML solutions on AWS, Azure, or GCP.
- Proficiency with Docker, Kubernetes, Git, Terraform, APIs, microservices, and cloud infrastructure.
- Strong understanding of SQL, data pipelines, feature engineering, feature stores, distributed systems, and system design.
- Experience with real-time and batch inference, model serving, model optimization, and production monitoring.
- Demonstrated ability to take ML solutions from research/PoC to reliable production systems.
- Strong knowledge of software engineering principles, testing, code quality, scalability, and performance optimization.
- Experience mentoring engineers and leading cross-functional technical initiatives.
- Strong problem-solving, communication, stakeholder management, and technical decision-making skills.
- Ability to balance model performance, engineering complexity, infrastructure cost, scalability, and business requirements.
- Bachelor's degree in Computer Science, AI/ML, Data Science, Engineering, Mathematics, Statistics, or a related field; Master's degree preferred.
- Experience with responsible AI, model governance, security, and data privacy is an advantage.

📌 ML Engineering Lead (India)
🏢 Good Co India
📍 India