Artificial Intelligence/Machine Learning Engineer - Generative AI (5-6 yrs)

Job Title : AI Engineer (AI-Powered Agents, Knowledge Graphs, & MLOps)

Location : Hyderabad

Job Type : Full-time

Hands on Gen AI development in GCP and Azure stack

Job Summary :

We are seeking an AI Engineer with deep expertise in building AI-powered agents, designing and implementing knowledge graphs, and optimizing business processes through AI-driven solutions. The role also requires hands-on experience in AI Operations (AI Ops), including continuous integration/continuous deployment (CI/CD), model monitoring, and retraining.

The ideal candidate will have experience working with open-source or commercial large language models (LLMs) and be proficient in using platforms like Azure Machine Learning Studio or Google Vertex AI to scale AI solutions effectively.

Key Responsibilities :

- AI Agent Development : Design, build, and deploy AI-powered agents for applications such as virtual assistants, customer service bots, and task automation systems using LLMs and other AI models.

- Knowledge Graph Implementation : Develop and implement knowledge graphs for enterprise data integration, enhancing the retrieval, structuring, and management of large datasets to support decision-making.

- AI-Driven Process Optimization : Collaborate with business units to optimize workflows using AI-driven solutions, automating decision-making processes and improving operational efficiency.

- AI Ops (MLOps) : Implement robust AI/ML pipelines that follow CI/CD best practices to ensure continuous integration and deployment of AI models across different environments.

- Model Monitoring and Maintenance : Establish processes for real-time model monitoring, including tracking performance, drift detection, and accuracy of models in production environments.

- Model Retraining and Optimization : Develop automated or semi-automated pipelines for model retraining based on changes in data patterns or model performance. Implement processes to ensure continuous improvement and accuracy of AI solutions.

- Cloud and ML Platforms : Utilize platforms such as Azure Machine Learning Studio, Google Vertex AI, and open-source frameworks for end-to-end model development, deployment, and monitoring.

- Collaboration : Work closely with data scientists, software engineers, and business stakeholders to deploy scalable AI solutions that deliver business impact.

- MLOps Tools : Leverage MLOps tools for version control, model deployment, monitoring, and automated retraining processes to ensure operational stability and scalability of AI systems.

- Performance Optimization : Continuously optimize models for scalability and performance, identifying bottlenecks and improving efficiencies.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

- 3+ years of experience as an AI Engineer, focusing on AI-powered agent development, knowledge graphs, AI-driven process optimization, and MLOps practices.

- Proficiency in working with large language models (LLMs) such as GPT-3/4, GPT-J, BLOOM, or similar, including both open-source and commercial variants.

- Experience with knowledge graph technologies, including ontology design and graph databases (e.g., Neo4j, AWS Neptune).

- AI Ops/MLOps Expertise : Hands-on experience with AI/ML CI/CD pipelines, automated model deployment, and continuous model monitoring in production environments.

- Familiarity with tools and frameworks for model lifecycle management, such as MLflow, Kubeflow, or similar.

- Strong skills in Python, Java, or similar languages, and proficiency in building, deploying, and monitoring AI models.

- Solid experience in natural language processing (NLP) techniques, including building conversational AI, entity recognition, and text generation models.

- Model Monitoring & Retraining : Expertise in setting up automated pipelines for model retraining, monitoring for drift, and ensuring the continuous performance of deployed models.

- Experience in using cloud platforms like Azure Machine Learning Studio, Google Vertex AI, or similar cloud-based AI/ML tools.

Preferred Skills :

- Experience with building or integrating conversational AI agents using platforms like Microsoft Bot Framework, Rasa, or Dialogflow.

- Familiarity with AI-driven business process automation and RPA integration using AI/ML models.

- Knowledge of advanced AI-driven process optimization tools and techniques, including AI orchestration for enterprise workflows.

- Experience with containerization technologies (e.g., Docker, Kubernetes) to support scalable AI/ML model deployment.

- Certification in Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or relevant MLOps-related certifications is a plus.