Technical Lead - AI & Data System

As a Tech Lead specializing in AI & Data Systems, your primary responsibility will be to design, build, and scale intelligent data-driven platforms. Your role will involve a combination of hands-on engineering, AI/ML system design, and technical leadership. You will lead the development of AI-powered data solutions, mentor engineers, and collaborate closely with product, analytics, and business stakeholders. Key Responsibilities: - Design, develop, and deploy scalable AI/ML-enabled data systems for analytics, prediction, and automation. - Build and optimize data pipelines for ingestion, processing, and transformation of structured and unstructured data. - Lead the integration of ML models into production systems with a focus on reliability, performance, and scalability. - Implement feature engineering pipelines, model inference workflows, and monitoring mechanisms. Machine Learning & AI Systems: - Develop and productionize ML models using frameworks like TensorFlow, PyTorch, Scikit-learn, or similar. - Manage ML lifecycle including model versioning, deployment, retraining, and performance tracking. - Apply AI techniques such as predictive modeling, NLP, computer vision, recommendation systems, and anomaly detection. - Ensure models are explainable, measurable, and aligned with business outcomes. Data Architecture & Engineering: - Design data architectures using data lakes, data warehouses, and streaming systems. - Utilize technologies like SQL/NoSQL databases, Spark, Kafka, Airflow, Snowflake/BigQuery/Redshift. - Ensure data quality, governance, and security across the data ecosystem. - Optimize data storage, query performance, and cost efficiency. Technical Leadership: - Act as a technical owner for AI & data initiatives from design to deployment. - Review architecture, code, and system designs to enforce best practices. - Mentor junior engineers on system design, ML concepts, and coding standards. - Collaborate with Product, Analytics, and Business teams to translate requirements into technical solutions. Cloud & DevOps: - Build and deploy AI/data systems on cloud platforms like AWS, Azure, or GCP. - Implement CI/CD pipelines for data and ML workflows, utilizing Docker and Kubernetes. - Monitor system performance, reliability, and scalability in production. Qualifications Required: - 3-7 years of experience in data engineering, AI/ML systems, or backend engineering. - Strong programming skills in Python (mandatory); familiarity with Java/Scala is a plus. - Hands-on experience with machine learning model development and deployment. - Solid understanding of data structures, algorithms, and distributed systems. - Experience working with big data frameworks like Spark, Hadoop, and streaming platforms. - Strong SQL skills and experience with both relational and NoSQL databases. - Knowledge of MLOps, model monitoring, and ML pipeline automation. - Experience with cloud-native data and ML services. - Familiarity with tools like Airflow, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents. This job role does not include any additional details about the company. As a Tech Lead specializing in AI & Data Systems, your primary responsibility will be to design, build, and scale intelligent data-driven platforms. Your role will involve a combination of hands-on engineering, AI/ML system design, and technical leadership. You will lead the development of AI-powered data solutions, mentor engineers, and collaborate closely with product, analytics, and business stakeholders. Key Responsibilities: - Design, develop, and deploy scalable AI/ML-enabled data systems for analytics, prediction, and automation. - Build and optimize data pipelines for ingestion, processing, and transformation of structured and unstructured data. - Lead the integration of ML models into production systems with a focus on reliability, performance, and scalability. - Implement feature engineering pipelines, model inference workflows, and monitoring mechanisms. Machine Learning & AI Systems: - Develop and productionize ML models using frameworks like TensorFlow, PyTorch, Scikit-learn, or similar. - Manage ML lifecycle including model versioning, deployment, retraining, and performance tracking. - Apply AI techniques such as predictive modeling, NLP, computer vision, recommendation systems, and anomaly detection. - Ensure models are explainable, measurable, and aligned with business outcomes. Data Architecture & Engineering: - Design data architectures using data lakes, data warehouses, and streaming systems. - Utilize technologies like SQL/NoSQL databases, Spark, Kafka, Airflow, Snowflake/BigQuery/Redshift. - Ensure data quality, governance, and security across the data ecosystem. - Optimize data storage, query performance, and cost efficiency. Technical Leadership: - Act as a technical owner for AI & data initiatives from design to deployment. - Review architecture, code, and system designs to enfo