GenAI Engineer

Key Responsibilities

• Lead the implementation and delivery of AI and Generative AI applications.
• Design end-to-end AI systems using commercial and open-source tools.
• Translate business requirements into scalable AI-driven solutions.
• Collaborate with data engineering teams to ensure data quality, governance, and smooth data flows.
• Define reference architectures, technical roadmaps, and best practices for AI applications.
• Design and manage data ingestion pipelines, model training environments, CI/CD, and monitoring systems.
• Use containerization and cloud services to deploy and scale AI systems.
• Ensure scalability, reliability, security, and maintainability of AI solutions.
• Provide technical mentorship and conduct knowledge-sharing sessions.

Requirements

• 7+ years of experience in AI, Machine Learning, or related roles.
• Strong experience building AI agents using LangGraph, AutoGen, or CrewAI.
• Proficiency in Python and ML/DL frameworks such as TensorFlow, PyTorch, Keras.
• Solid understanding of Deep Learning and NLP (RNN, CNN, LSTM, Transformers).
• Experience with cloud platforms (AWS, Azure, or GCP) for AI deployments.
• Strong knowledge of distributed systems, REST APIs, GraphQL, and microservices.
• Familiarity with event-driven architectures and message brokers (Kafka, RabbitMQ).

Good to Have

• Experience with Docker, Kubernetes, and CI/CD tools (Jenkins, GitLab).
• Knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB, Cassandra).
• Experience with Infrastructure as Code (Terraform, CloudFormation).
• Hands-on exposure to Hugging Face, OpenAI APIs, and other LLM tools.
• Experience with MLOps / LLMOps, model training, and fine-tuning (GPT-4, LLaMA, PaLM).
• Familiarity with monitoring and logging tools (Prometheus, Grafana, ELK).