AI​/ML Lead Engineer

We are looking for an experienced AI/ML Lead Engineer to lead the design, development, and deployment of scalable machine learning and artificial intelligence solutions. The ideal candidate will have strong hands‑on experience in machine learning, deep learning, generative AI, MLOps, and cloud technologies, along with the ability to lead technical teams and drive AI initiatives from concept to production.

• Lead the architecture, development, and implementation of end‑to‑end AI/ML solutions.
• Design and develop machine learning models for classification, prediction, recommendation, NLP, computer vision, or other business use cases.
• Lead the development and integration of Generative AI, LLM, RAG, and agent‑based solutions where applicable.
• Evaluate and select appropriate ML algorithms, frameworks, tools, and technologies.
• Develop scalable ML pipelines for data preparation, model training, validation, deployment, and monitoring.
• Establish and implement MLOps best practices for model lifecycle management, CI/CD, automation, and production monitoring.
• Work with cloud platforms such as AWS, Azure, or GCP to build and deploy AI/ML solutions.
• Lead technical discussions, architecture reviews, and proof‑of‑concept initiatives.
• Mentor and guide junior and senior engineers, promoting engineering best practices.
• Collaborate with data engineers, software engineers, product managers, and business stakeholders to translate business requirements into technical solutions.
• Monitor model performance, reliability, scalability, and cost in production environments.

• Bachelor’s or master’s degree in computer science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field.
• 8+ years of experience in software engineering, machine learning, data science, or related areas, with significant hands‑on AI/ML experience.
• Strong programming skills in Python and experience with software engineering best practices.
• Strong knowledge of machine learning and deep learning concepts, algorithms, and statistical techniques.
• Hands‑on experience with frameworks such as PyTorch, Tensor Flow, Scikit‑learn, or similar
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• Experience building and deploying production‑grade