Experience:

• 5 to 8 years of experience applying analytical methods and AI/ML solutions in enterprise environments
• 5 to 8 years of experience using Python-based AI/ML technologies
• Experience leading AI or Data Science teams
• Experience acting as a senior technical lead facilitating solution trade-offs and architectural decisions
• Experience using Cloud AI Platforms, with GCP preferred
• Hands-on experience with Generative AI technologies and enterprise AI deployment

Qualification:

• Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Statistics, Applied Mathematics, IT, or equivalent

Responsibilities:

• Define and govern AI project lifecycles from data acquisition through experimentation, production deployment, monitoring, and continuous optimization
• Lead AI engineers and data scientists
• Establish best practices and drive enterprise-grade AI adoption using modern MLOps and LLMOps principles
• Partner with business leaders to identify high-impact AI opportunities and translate them into scalable AI/ML solutions
• Define and communicate AI product vision, roadmaps, and measurable success metrics
• Drive AI strategy across predictive analytics, Generative AI, and intelligent automation initiatives
• Lead cross-functional AI programs and influence executive stakeholders through insights and presentations
• Architect and oversee end-to-end AI/ML and GenAI systems
• Support architectural reviews and ensure best practices across AI systems
• Own end-to-end AI product delivery in partnership with Product, Engineering, and Data teams
• Ensure production-grade deployment of AI models using containerization, orchestration, and scalable cloud infrastructure
• Influence investment decisions using measurable impact metrics and ROI analysis
• Establish monitoring frameworks for model drift, performance degradation, and system reliability
• Build AI engineering standards, reusable frameworks, and shared tooling across SSDA
• Promote knowledge sharing through Communities of Practice
• Foster a culture of experimentation, continuous learning, and engineering excellence
• Support talent development in emerging AI domains including GenAI and agent-based systems

Additional responsibilities:

• Establish governance frameworks for Responsible AI, model explainability, fairness, and compliance
• Implement scalable MLOps and LLMOps practices including CI/CD for ML, model versioning, monitoring, and automated retraining
• Apply strong software engineering practices within AI systems including testing, modular design, observability, and documentation
• Drive research and innovation in advanced AI techniques to enhance enterprise capabilities
• Implement Responsible AI principles including governance, model explainability, fairness, and ethical AI compliance

Nice to have:

• Master’s or PhD in Data Science, Machine Learning, Statistics, Applied Mathematics, or Computer Science
• Experience managing and growing high-performing AI teams
• Expert-level knowledge in advanced predictive analytics and AI techniques such as Genetic Algorithms, Ensemble Learning, Neural Networks, NLP, Simulation, and Design of Experiments
• Strong working knowledge of GCP and enterprise AI architecture patterns
• Expertise in open-source technologies such as Python, R, Spark, and SQL
• Experience building enterprise-grade GenAI and agent-based AI solutions

More skills:

AI/ML, predictive analytics, intelligent automation, machine learning algorithms, ensemble methods, neural networks, regression models, simulation, optimization techniques, NLP, image processing, TensorFlow, PyTorch, Keras, Python, foundation models, evaluation pipelines, CI/CD, model versioning, monitoring, automated retraining, Git, Docker, API-based deployments, software engineering, testing, modular design, observability, documentation, Responsible AI, model explainability, fairness, ethical AI, multi-agent orchestration

Other:

• Job ID 66985
• Category: Enterprise Technology
• Work Type: Hybrid
• AI systems include predictive analytics models, deep learning and neural networks, NLP and computer vision solutions, RAG systems, and agentic AI frameworks