AI/ML Technical Architect

Role details

Experience requirements

• 8+ years in software engineering, data, or AI systems.
• 2+ years of hands-on experience with generative AI and LLM-based solutions.
• 4+ years designing and architecting enterprise-scale platforms and distributed systems.

Preferred qualifications

• Master’s or PhD in Computer Science, AI, ML, or a related field; open-source AI contributions or research publications are preferred.
• Multi-modal or cross-modal systems, MLOps, full model lifecycle management, compliant AI systems, and enterprise AI transformation leadership are preferred.
• Cloud certifications focused on AI/ML for AWS, Azure, or GCP are preferred.

Required technical skills

• Deep experience with GPT-4, Claude, LLaMA, and enterprise or open-source LLM ecosystems; expertise deploying and optimizing Phi-3, Gemma, and TinyLlama.
• Advanced agent frameworks: LangChain, LangGraph, Semantic Kernel, and Agno; RAG, vector databases, semantic retrieval, and hybrid retrieval.
• Fine-tuning includes LoRA, QLoRA, DoRA, AdaLoRA, prompt-tuning, adapters, prefix tuning, and P-tuning v2; RLHF/RLAIF pipelines.
• Compression includes INT8/INT4 quantization, GPTQ, AWQ, GGML, pruning, and distillation.
• Multi-environment deployment across cloud, edge, and on-premises; autoscaling, rate limiting, resource governance, real-time inference, and streaming pipelines.
• TensorFlow, PyTorch, Hugging Face, LlamaIndex, API-driven design, and full-stack AI service development.
• AWS, Azure, and GCP AI platforms; CI/CD, Docker, Kubernetes, monitoring, and observability.

Technical competencies assessed

• Distributed AI system architecture and design; production code quality, scalability, and performance optimization.
• Model benchmarking, evaluation, and cost optimization.
• Security, privacy, and AI governance engineering.
• Enterprise-grade deployment and scaling strategies.