Job Description

Design, build, and maintain machine learning pipelines, ensuring continuous integration and deployment (CI/CD) of models in production environments. Deploy machine learning models as APIs, microservices, or serverless functions for real-time inference. Manage and scale machine learning workloads using Kubernetes, Docker, and cloud-based infrastructure (AWS, Azure, GCP). Automation & Scripting: Automate routine tasks across the ML lifecycle (data preprocessing, model training, evaluation, deployment) using Python, Bash, and other scripting tools. Implement automation for end-to-end model management and monitor pipelines for health, performance, and anomalies. Cloud Platforms & Infrastructure: Utilize cloud platforms (AWS, Azure, GCP) to optimize the scalability, performance, and cost-effectiveness of ML systems. Leverage Infrastructure as Code (IaC) tools like Terraform or CloudFormation to provision and manage cloud resources effectively. Data Pipelines & Integration: Build and maintain robust data pipelines to streamline data ingestion, preprocessing, and feature engineering. Work with both structured and unstructured data sources and databases (SQL, NoSQL) to feed data into ML models. Monitoring, Logging & Troubleshooting: Set up monitoring and logging systems to track model performance, detect anomalies, and maintain system health. Diagnose and resolve issues across the machine learning pipeline and deployed models. Collaboration & Communication: Collaborate closely with data scientists, software engineers, and business stakeholders to ensure machine learning models meet the required business objectives and performance standards. Effectively communicate complex ML concepts and technical details to non-technical stakeholders. GenAI & AI Agents Expertise: Stay up to date with the latest trends in Generative AI (e.g., GPT models, Diffusion models) and AI agents, and bring this expertise into production environments. Design and deploy advanced GenAI solutions, ensuring they are aligned with business needs and ethical AI principles. Security & Compliance: Implement robust security measures for machine learning models and ensure compliance with relevant data protection and privacy regulations. Address vulnerabilities, ensuring safe and secure deployment of models in production environments. Optimization & Cost Management: Optimize machine learning resources (compute, memory, storage) to achieve high performance while minimizing operational costs. Regularly review and improve the efficiency of machine learning workflows. Testing & Validation: Develop and execute rigorous testing and validation strategies to ensure the reliability, accuracy, and fairness of deployed models. Use automated testing frameworks to continuously validate model performance. Required Skills & Qualifications: Education : Bachelor s or Master s degree in Computer Science, Engineering, Data Science, or a related field. Experience : Proven experience (3+ years) in machine learning engineering, MLOps, or related fields. Experience with deploying and managing machine learning models in production using tools like Kubernetes, Docker, and CI/CD pipelines. Hands-on experience with cloud platforms (AWS, Azure, GCP) and infrastructure automation tools (Terraform, CloudFormation). Strong coding experience in Python, Bash, or other scripting languages. Expertise in Generative AI models (e.g., GPT, GANs) and their deployment at scale. Experience working with databases (SQL, NoSQL) and building data pipelines. DevOps & CI/CD : Knowledge of DevOps tools and practices, including version control (Git), automated testing, and continuous integration/deployment. AI Agents : Familiarity with the latest AI agent frameworks and their deployment in real-world applications. Data Science Concepts : Solid understanding of GenAI,NLP, Computer Vision, machine learning algorithms, data structures, and model evaluation techniques. Problem-Solving : Strong troubleshooting and debugging skills to quickly identify and fix issues within ML pipelines and deployments. Collaboration & Communication : Excellent communication skills with the ability to work in a cross-functional team and explain technical concepts to non-technical stakeholders. Preferred Qualifications: Certification in Cloud Technologies (AWS, Azure, GCP) and MLOps platforms. Experience with large-scale ML systems and distributed computing. Understanding of ethical AI practices and AI fairness. Familiarity with cutting-edge AI technologies like reinforcement learning, AI agents, and deep learning. Technical Skills: Proficiency in Python, R, or other relevant programming languages. Strong knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras. Experience with SQL and cloud-native data processing tools (e.g., AWS Redshift, Azure Synapse, Spark). Familiarity with DevOps practices and CI/CD pipelines for ML model deployment. Soft Skills: Strong communication skills with the ability to translate complex technical concepts into business-friendly language. Problem-solving mindset, with the ability to approach challenges creatively and collaborate with diverse teams. Leadership potential or experience mentoring junior team members. Preferred Qualifications: Certification or training in AWS (e.g., AWS Certified Machine Learning), Azure, or other cloud services. Experience working with containerization technologies like Docker and Kubernetes for model deployment. Exposure to the latest trends in AI ethics, explainability, and fairness.