Radial Inc.
Website:
radial.com
Job details:
We are seeking a highly capable Senior AI Engineer to design, build, and scale enterprise-grade AI solutions across Machine Learning, Generative AI, and Agentic AI domains. This role requires strong hands-on expertise in ML model development, LLM orchestration, RAG pipelines, autonomous AI agents, and production deployment. You will play a critical role in transforming business operations through intelligent automation, predictive systems, and AI-powered decision-making platforms.Key Responsibilities AI / ML Engineering Design and deploy scalable machine learning solutions for forecasting, classification, anomaly detection, NLP, and optimization. Build end-to-end ML pipelines from ingestion, feature engineering, training, deployment, and monitoring. Fine-tune, evaluate, and optimize models using frameworks such as PyTorch, TensorFlow, XGBoost, Scikit-learn. Generative AI / Agentic AI Build LLM-powered enterprise solutions using GPT, Claude, LLaMA, Mistral, Gemini, etc. Develop autonomous AI agents capable of planning, reasoning, workflow execution, and tool usage. Implement multi-agent systems using CrewAI, AutoGen, LangGraph, LangChain, LlamaIndex. Build Retrieval-Augmented Generation (RAG) architectures using vector databases. Production & Platform Deploy AI workloads using Kubernetes, Docker, CI/CD, MLOps pipelines. Build APIs and scalable inference services. Implement monitoring for latency, hallucination risk, drift, token usage, and ROI. Business Collaboration Partner with product owners, operations teams, and leadership to identify high-value AI opportunities. Translate business problems into deployable AI solutions. Required Skills Strong Python expertise ML frameworks: TensorFlow / PyTorch / Scikit-learn LLM frameworks: LangChain / LlamaIndex / CrewAI / AutoGen Vector DBs: Pinecone / FAISS / Chroma / Weaviate Cloud: AWS / Azure / GCP APIs: FastAPI / Flask / REST / gRPC MLOps: MLflow / Kubeflow / SageMaker SQL / Spark / Data Engineering fundamentals Strong software engineering discipline
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