Role & Responsibilities :

- Lead, mentor, and manage a team of Machine Learning Engineers, Software Engineers, and/or Data Scientists.

- Own the end-to-end development and delivery of machine learning products and systems, from problem definition and experimentation to production deployment and monitoring.

- Define technical direction, architecture, engineering standards, and best practices for ML systems.

- Partner closely with Product, Data Science, Engineering, and Business teams to translate business problems into scalable ML solutions.

- Drive the design and implementation of robust ML pipelines, model-serving infrastructure, data pipelines, and MLOps practices.

- Ensure ML models and systems are reliable, scalable, maintainable, secure, and production-ready.

- Establish engineering processes around code quality, testing, CI/CD, model validation, monitoring, observability, and incident management.

- Review technical designs and code, identify engineering risks, and guide the team toward effective technical solutions.

- Set team goals, define priorities, manage execution, and ensure timely delivery of high-impact projects.

- Develop engineers through regular feedback, mentoring, performance management, and career development.

- Stay current with advances in machine learning, generative AI, LLMs, MLOps, and related technologies, and evaluate their applicability to business problems.

- Collaborate with senior leadership to define the ML engineering roadmap, resource requirements, and technical strategy.

- Drive a culture of experimentation, innovation, ownership, engineering excellence, and continuous improvement.

Preferred Candidate Profile :

- 5 - 10 years of experience in software engineering, machine learning engineering, data science engineering, or a closely related field.

- Proven experience leading or managing engineering/ML teams, ideally with direct people-management responsibility.

- Strong hands-on experience with Python and modern software engineering practices.

- Strong understanding of machine learning algorithms, model development, evaluation, and deployment.

- Experience building and operating production-grade ML systems at scale.

- Strong knowledge of MLOps, including model deployment, monitoring, CI/CD, experiment tracking, feature/data pipelines, and model lifecycle management.

- Experience with cloud platforms such as AWS, GCP, or Azure.

- Familiarity with technologies such as Docker, Kubernetes, APIs, distributed systems, SQL/NoSQL databases, and data processing frameworks.

- Experience with one or more ML/AI frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, or equivalent.

- Exposure to Generative AI, LLMs, RAG, vector databases, or AI agents is a strong plus.

- Strong system-design and architectural thinking, with the ability to balance technical quality, scalability, delivery timelines, and business priorities.

- Excellent communication and stakeholder-management skills, with the ability to work effectively across engineering, product, data, and business teams.

- Demonstrated ability to hire, mentor, retain, and develop high-performing engineering talent.

- Strong ownership mindset with the ability to operate effectively in a fast-paced, ambiguous environment.

- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical field is preferred.