Job Summary

Startup Inno is seeking a highly skilled and visionary ML Engineering Lead to spearhead the production and deployment of advanced AI models. This role requires a unique blend of machine learning expertise, software engineering excellence, and leadership capability to ensure scalable, reliable, and efficient AI solutions are delivered across our products. The ideal candidate will drive the transition of machine learning prototypes into robust, production-ready systems while mentoring a growing team of ML engineers and data scientists.

Key Responsibilities

• Lead the end-to-end lifecycle of machine learning models, from research and prototyping to deployment and monitoring in production environments.

• Collaborate closely with Data Science, Product, and Engineering teams to integrate AI solutions into real-world applications.

• Design and implement scalable ML pipelines and MLOps practices, including model versioning, testing, and automated deployment workflows.

• Ensure high standards of code quality, model performance, and system reliability.

• Drive architectural decisions for ML infrastructure, cloud integration, and performance optimization.

• Mentor, guide, and develop a team of ML engineers, fostering best practices and knowledge sharing.

• Stay ahead of industry trends in AI, ML frameworks, and productionization techniques to maintain competitive advantage.

Required Skills and Qualifications

• Advanced proficiency in Python, TensorFlow, PyTorch, or other ML frameworks.

• Strong experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes).

• Solid understanding of software engineering principles, APIs, and microservices architecture.

• Proven ability to optimize ML models for performance, scalability, and maintainability.

• Strong analytical, problem-solving, and debugging skills.

• Excellent communication and collaboration skills with cross-functional teams.

Experience

• Bachelors or Masters degree in Computer Science, AI, Machine Learning, or a related field; PhD is a plus.

• 5+ years of hands-on experience in machine learning engineering, with at least 2 years in a leadership or senior engineering role.

• Demonstrated track record of successfully deploying ML models to production in a commercial or large-scale environment.

Working Hours

• Full-time role (40–45 hours per week)

• Flexible working arrangements, including hybrid or remote options, with occasional team meetings or on-site collaboration as needed.

Knowledge, Skills, and Abilities (KSA)

• Deep understanding of ML algorithms, model training, hyperparameter tuning, and evaluation metrics.

• Strong knowledge of cloud platforms (AWS, GCP, or Azure) for ML deployment.

• Ability to balance research innovation with practical deployment constraints.

• Leadership mindset: capable of guiding teams, setting priorities, and inspiring high performance.

• Adaptability to a fast-paced startup environment and willingness to take ownership of complex problems.

Benefits

• Competitive salary with performance-based bonuses.

• Stock options and equity participation.

• Comprehensive health, dental, and vision insurance plans.

• Generous paid time off and flexible working schedule.

• Learning and development support including conferences, workshops, and certifications.

Why Join Startup Inno

Startup Inno is at the forefront of AI innovation, developing transformative solutions that impact industries globally. Joining our team means contributing to cutting-edge AI applications, shaping our ML strategy, and leading a talented engineering team in a collaborative and fast-growing startup environment. We value creativity, ownership, and the drive to solve challenging problems.

How to Apply

Interested candidates are invited to submit their resume, cover letter, and a portfolio of relevant ML projects to us. Please include ML Engineering Lead – Productionizing AI Models in the subject line. Shortlisted candidates will be contacted for a multi-stage interview process, including technical assessments and leadership evaluation.