Radiansys Inc.
Website:
radiansys.com
Job details:
Position Name: AI Lead/Architect
Location: Hyderabad, Bangalore, Chennai (Hybrid - 3 days from Office)
Experience: 6-15 Years
We are looking for an experienced AI Lead/AI Architect with hands-on expertise in Generative AI and Agentic AI to design, develop, and deploy production-grade, enterprise-scale AI solutions. The ideal candidate should have strong proficiency in Python, Machine Learning, and multi-agent system orchestration, with proven experience delivering end-to-end implementations with minimal supervision.
Key Responsibilities
- Design, build, and orchestrate multi-agent systems capable of autonomous decision-making and task execution.
- Lead the development and deployment of Generative AI solutions using LLMs and fine-tuned models.
- Implement RAG (Retrieval-Augmented Generation) pipelines with robust document parsing, re-ranking, and context optimization.
- Integrate AI agents into enterprise systems through APIs, function calling, and workflow orchestration frameworks (e.g., LangGraph, CrewAI, LlamaIndex, Haystack).
- Fine-tune and evaluate LLMs (using LoRA, PEFT, or QLoRA) for domain-specific use cases.
- Collaborate cross-functionally with data, platform, and DevOps teams to ensure scalable and secure AI deployments.
- Ensure production-grade quality performance optimization, monitoring, and continuous improvement.
- Provide technical mentorship to junior engineers (minimal team handling required).
Required Skills & Experience
- 7 -15 years of total experience, with Min 3+ years in Generative AI and 1+ years Agentic AI with Min 2 or 3 Production-grade implementation at Enterprise Level.
- Strong background in Machine Learning, Deep Learning, and Python programming.
- Hands-on experience with LLM frameworks (LangChain, LlamaIndex, Haystack, Semantic Kernel, etc.).
- Proficiency in multi-agent orchestration (CrewAI, LangGraph, Swarm, Autogen, or custom frameworks).
- Expertise in vector databases (FAISS, Pinecone, Chroma, Weaviate, etc.) and embedding models.
- Proven fine-tuning experience using LoRA, QLoRA, or PEFT.
- Experience in enterprise-grade GenAI implementations — from PoC to production.
- Strong understanding of RAG architecture, document chunking, context optimization, and model evaluation.
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- Excellent problem-solving and debugging skills.
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