
Artificial Intelligence/Machine Learning Lead
2 weeks ago
About the Role :
We are seeking an experienced AI/ML Lead to spearhead the design, development, and deployment of cutting-edge artificial intelligence and machine learning solutions. You will work at the intersection of data science, engineering, and product, leading teams to solve complex business problems using advanced AI/ML techniques. The ideal candidate will have a strong technical background, proven leadership skills, and the ability to drive innovation from research to production at scale.
Key Responsibilities :
Strategy & Leadership :
- Lead and mentor a high-performing team of ML engineers, data scientists, and researchers.
- Foster a culture of innovation, collaboration, and continuous learning within the AI/ML team.
- Evaluate emerging AI technologies and tools, recommending adoption where beneficial.
Solution Development :
- Oversee the full ML lifecycle: data collection, preprocessing, feature engineering, model selection, training, evaluation, and deployment.
- Ensure reproducibility, scalability, and maintainability of ML models in production.
- Drive the application of Generative AI, LLMs, Computer Vision, NLP, or other advanced techniques depending on business needs.
Collaboration & Stakeholder Management :
- Communicate complex technical concepts to non-technical stakeholders effectively.
- Collaborate with DevOps/DataOps to ensure reliable, secure, and efficient ML pipelines and deployments.
Governance & Best Practices :
- Ensure compliance with ethical AI practices, data privacy regulations (GDPR, CCPA, etc.), and security standards.
- Define and monitor KPIs for AI/ML initiatives, ensuring measurable business impact.
Qualifications :
Education : Bachelors/Masters/Ph.D. in Computer Science, Data Science, AI/ML, or a related field.
Experience :
- 5 years of experience in software/data engineering or AI/ML roles.
- Proven track record of deploying ML models into production at scale.
Technical Skills :
- Experience with Generative AI/LLMs (e.g., GPT, LLaMA, Hugging Face Transformers).
- Solid understanding of NLP, Computer Vision, or Recommendation Systems.
- Strong knowledge of MLOps, CI/CD for ML, and deployment frameworks (e.g., MLflow, Kubeflow, SageMaker).
- Proficiency with big data and cloud platforms (AWS, GCP, Azure).
- Hands-on expertise in SQL/NoSQL databases and distributed data systems (Spark, Hadoop, etc.).
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