Interesting Job Opportunity: AI/ML Engineer

AI Model Design And Build

• Work closely with data scientists and business to design and implement AI algorithms, frameworks and architectures.

• Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and load data from various sources.

• Integrate structured and unstructured data from internal and external systems into centralized data platforms.

• 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 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.

• Collaborate extensively with data scientists, and business during model development and deployment.
• Maintain updated documentation with details of all aspects of model development lifecycle.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

(ref:hirist.tech)