Key Responsibilities
• Architectural Leadership: Design, architect, and scale robust Generative AI solutions, in cluding complex RAG pipelines, multi-agent systems, and customized foundation models.
• Model Engineering & Fine-Tuning: Evaluate state-of-the-art open-source and proprietary LLMs (e.g., GPT-4, Gemini, Llama 3, Claude). Lead the fine-tuning (PEFT, LoRA, QLoRA) and alignment (RLHF, DPO) of models for domain-specific enterprise use cases.
• System Optimization: Optimize LLM inference for latency, throughput, and cost using ad vanced techniques such as quantization, vLLM, TensorRT-LLM, and prompt caching.
• LLMOps & Infrastructure: Establish enterprise-grade LLMOps pipelines for continuous in tegration, deployment, monitoring, and evaluation (e.g., managing model drift, hallucination tracking, and guardrails).
• Technical Mentorship: Mentor and guide mid-level and junior AI engineers, fostering a cul ture of technical excellence, continuous learning, and rigorous code quality.
• Cross-Functional Collaboration: Partner seamlessly with Product Managers, Data Scientists, and Cloud Architects to translate business requirements into scalable, secure, and responsible AI products.
Required Qualifications
• Experience: 8–10 years of hands-on industry experience in Software Engineering, Machine Learning, or Applied AI, with at least 2+ years completely dedicated to Generative AI, LLMs, and NLP.
• Programming: Expert-level proficiency in Python and writing production-ready, highly opti mized, and maintainable code.
• AI/ML Frameworks: Deep expertise in deep learning frameworks (PyTorch, TensorFlow) and the modern GenAI stack (Hugging Face Transformers, LangChain, LlamaIndex).
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• Vector Infrastructure: Extensive experience implementing and scaling Vector Databases (e.g., Pinecone, Milvus, Weaviate, or Qdrant) for high-performance retrieval systems.
• Cloud & Deployment: Proven track record of deploying ML models in production environ ments using AWS, GCP, or Azure, leveraging containerization (Docker, Kubernetes) and server less architectures.
• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Mathe matics, or a highly related technical field.
Preferred Qualifications (Nice to Have)
• Experience building autonomous AI agents and tool-use (function calling) workflows.
• Strong understanding of Data Security, Privacy, and Responsible AI principles (e.g., handling PII in LLM pipelines, implementing AI guardrails like NeMo Guardrails).
• Open-source contributions to major AI/ML libraries or published research papers in top-tier AI conferences (NeurIPS, ACL, EMNLP).
• Experience with multi-modal Generative AI (image, audio, and video generation models).
To apply, please submit your resume, portfolio/GitHub, and a brief description of the most complex AI system you have architected into production.