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Optimize inference for latency, throughput, and cost.\n\n- LLMs and SLMs : Fine-tune and deploy open and closed models using techniques such as LoRA/QLoRA, PEFT, instruction tuning, and preference tuning (RLHF/DPO). Apply quantization and distillation where needed.\n\n- Agentic systems : Design and productionize agentic frameworks RAG pipelines, tool/function calling, memory, planning loops, and multi-agent orchestration with appropriate guardrails and observability.\n\n- Quality and trust : Build evaluation frameworks (offline + online, including LLM-as-judge and red-teaming). Diagnose and mitigate hallucinations, bias, and drift.\n\n- Rapid innovation : Track SOTA research, prototype quickly, and showcase work through demos and tech talks to internal stakeholders and leadership.\n\nAI Engineer (Python, GenAI/LLMs + ML Fundamentals) : \n\nRequired Qualifications : \n\n- 5+ years of hands-on experience as an AI/ML Engineer or Applied Scientist, with proven production deployments including at least one LLM-based or agentic system taken to production.\n\n- Strong Python skills and solid software engineering fundamentals (version control, testing, design patterns, code reviews).\n\n- Deep Learning & NLP : Strong grasp of transformer architectures, attention, tokenization, embeddings, and modern NLP techniques. 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