Python Professional -GenAI-LLMOps

Educational Requirements

• Bachelor of Engineering, BTech, BCA, BSc, MTech, MSc, MCA

Service Line

• Data Analytics Unit

Responsibilities

• Key ResponsibilitiesDevelop and deploy Generative AI applications using LLMs (GPT, Llama, etc.)
• Build and maintain LLM pipelines, including prompt engineering and fine-tuning
• Design and implement end-to-end AI/ML workflows (data model deployment)
• Work with vector databases for semantic search and retrieval (RAG architecture)
• Implement LLMOps practices for monitoring, evaluation, and versioning
• Integrate LLMs into applications via APIs and microservices
• Optimize model performance, cost, and latency
• Collaborate with data engineers, product teams, and stakeholders
• Conduct testing, validation, and debugging of AI models
• Ensure security, compliance, and responsible AI practices

Additional Responsibilities

• Experience with fine-tuning LLMs / parameter-efficient tuning (LoRA, PEFT)
• Knowledge of multimodal AI (text, image, audio)
• Exposure to cloud platforms (AWS, Azure, GCP)
• Experience with Kubernetes and scalable deployments
• Knowledge of data engineering tools (Spark, Kafka)
• Familiarity with vector search optimization and embeddings
• Understanding of AI governance, ethics, and compliance
• Experience with chatbots, copilots, or conversational AI systems

Technical and Professional Requirements

• Primary skills:Technology- >Data Science- >Machine Learning, Technology- >Geographical Information System- >Spatial Databases- >SQL Server, Technology- >Machine Learning- >Python

Preferred Skills

• Technology- >AI-Data science- >PYTHON
• Technology- >AI-Data science- >Machine Learning
• Technology- >AI-Generative AI- >Generative AI - Basic- >retrieval augmented generation (rag)