Live opening · Posted 1 day ago
The key details from the original listing.
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Description supplied by the original job listing.
The core requirements for the job include the following:
AI / GenAI / LLM:
Strong hands-on experience with Large Language Models (LLMs) such as GPT, Claude, and Gemini.
Experience in building generative AI applications using RAG (Retrieval-Augmented Generation).
Expertise in prompt engineering, prompt tuning, and evaluation frameworks.
Experience with fine-tuning techniques such as LoRA, PEFT, and model optimization.
Hands-on experience with OpenAI, Azure OpenAI, AWS Bedrock, or similar APIs.
Agentic AI:
Experience in building AI agents and multi-agent systems.
Hands-on experience with LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, and Semantic Kernel.
Understanding of tool calling, function calling, and agent orchestration.
Experience with memory systems, context handling, and reasoning workflows.
Ability to design planning agents and autonomous decision-making systems.
Architecture and Engineering:
Strong experience in AI system architecture and scalable distributed systems.
Experience with microservices architecture, REST APIs, and event-driven systems.
Ability to design real-time and batch AI pipelines.
Experience in building production-grade AI platforms and services.
MLOps / LLMOps:
Experience with MLflow, Kubeflow, Databricks, and SageMaker.
Strong understanding of CI/CD pipelines for AI systems.
Experience in model deployment, monitoring, drift detection, and observability.
Experience in model versioning and lifecycle management.
Data and Retrieval:
Experience with vector databases such as FAISS, Pinecone, Weaviate, and Chroma.
Strong understanding of embeddings, semantic search, and retrieval pipelines.
Experience with structured and unstructured data processing.
Knowledge of data pipelines and ETL processes.
Programming and Cloud:
Strong proficiency in Python.
Experience with PyTorch, TensorFlow, and Hugging Face Transformers.
Experience with FastAPI or Flask for building AI services.
Hands-on experience with Azure, AWS, or GCP.
Experience with Docker and Kubernetes.
Understanding of GPU-based workloads and scalable infrastructure.
Experience
6-10 yrs
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