Development and deployment of Generative AI applications using LLMs, RAG architectures, prompt engineering, Agentic AI frameworks, NLP techniques, and workflow orchestration tools.
Own model lifecycle management, including training, deployment, monitoring, explainability, validation, governance, and performance optimization.
Design and implement scalable cloud-based data science and AI solutions across AWS, GCP, Azure, or equivalent platforms.
Perform advanced statistical analysis, experimentation, feature engineering, forecasting, and root-cause analysis to enhance model accuracy and business outcomes.
Develop and maintain high-quality, reusable, and well-tested code following engineering best practices.
Technical troubleshooting and resolution of complex data, model, and pipeline issues in production environments.
Establish and promote best practices in data science, machine learning engineering, MLOps, and AI governance.
Collaborate with business stakeholders, product teams, engineering teams, and leadership to define roadmaps and technical strategies.
Mentor junior Data Scientists and contribute to knowledge sharing, onboarding, code reviews, and technical leadership within the team.
Communicate technical concepts, insights, risks, assumptions, and recommendations effectively to both technical and non-technical audiences.
Requirements
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
2-4 years of experience in Data Science, Machine Learning, AI, or related analytical disciplines.
Advanced expertise in Python and strong proficiency in SQL. • Strong knowledge of Machine Learning, Statistical Modelling, Deep Learning, Feature Engineering, and Model Evaluation techniques.
Experience building and deploying production-scale machine learning solutions.
Expertise in forecasting, time-series modelling, predictive analytics, anomaly detection, and advanced analytics techniques.
Hands-on experience with machine learning frameworks such as Scikit-learn, XGBoost, TensorFlow, PyTorch, Prophet, DeepAR, or equivalent.
Strong understanding of MLOps practices, including CI/CD, model deployment, monitoring, governance, and automation.
Experience with workflow orchestration and data engineering tools such as Airflow, Spark, PySpark, and ETL/ELT frameworks.
Experience with cloud platforms such as AWS, GCP, or Azure, including cloud-native ML and data services.
Strong knowledge of Generative AI technologies
Benefits
Performance-based annual bonus
Performance rewards and recognition
Agile Benefits - special allowances for Health, Wellness & Academic purposes
Paid birthday leave Team engagement allowance
Comprehensive Health & Life Insurance Cover - extendable to parents and in-laws
Hybrid work arrangement
Sysco LABS is an Equal Opportunity Employer.