ML Engineer

ML Engineer

Key Responsibilities :

• AI Model design and build: Work closely with data scientists and business to design and implement AI algorithms, frameworks and architectures.
• AI model Data Preprocessing: Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and load data from various sources.
• AI model Feature Engineering: Integrate structured and unstructured data from internal and external systems into centralized data platforms.
• Performance Tuning of AI/ models: Optimize data workflows and queries for performance, scalability, and cost-efficiency. Building Agentic Systems: Developing intelligent AI agents that can reason, plan, and execute tasks autonomously using LLMs and other tools.
• LLM application Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques like parameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generation pipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage and retrieval of embeddings generated by LLMs. rafting effective prompts to elicit desired responses from LLMs. Connecting LLMs and generative models with other systems and APIs to create comprehensive solutions.
• Communicate findings: Collaborate extensively with data scientists, and business during model development and deployment. Maintain updated documentation with details of all aspects of model development lifecycle.
• Responsible AI: Build AI systems which are trustworthy and beneficial considering ethical principles such as fairness, transparency, accountability, privacy and reliability. Implement quantifiable metrics detecting bias, explainability and adherence to regulatory compliance.
• AI Model Deployment and Lifecycle Management: Orchestrate robust and error-free deployment of AI models into production environments, making them accessible to applications and users. Ensure that models are deployed securely in compliance with relevant regulations.
• Automation and Pipeline Management: Create and manage automated pipelines for AI/ workflows including training, testing and deployment. Accelerate the AI model lifecycle ensuring continuous availability of updated and optimized model algorithms, reducing manual errors. Implement CI/CD pipelines to automate the testing and deployment of new model versions, enabling updates reducing manual intervention.
• Monitoring and Maintenance: Set up monitoring systems to track key metrics such as prediction accuracy, response times, resource utilization, and error rates of deployed models. Identify and troubleshoot issues, ensuring the models continue to perform as expected.
• Infrastructure Management: Manage the infrastructure required for training, testing, and running AI models in production, including provisioning hardware and software resources, leveraging cloud platforms and containerization technologies like Docker and Kubernetes.
• Data and Model Versioning and Rollback: Implement version control for data and models, allowing for tracking changes, testing older versions, and ensuring reproducibility. Establish data governance practices and experiment tracking for auditing and compliance purposes.
• Collaboration and Communication: Collaborate extensively with data scientists, software engineers, and DevOps teams to ensure smooth integration AI models. Maintain updated documentation with details of all aspects of model deployment and lifecycle.