AI/ML Technical Architect

Role overview

Experience requirements

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

Preferred qualifications

• A Master’s or PhD in Computer Science, AI, ML, or a related field is preferred.
• Open-source AI contributions or research publications are preferred.
• Multimodal and cross-modal systems experience is preferred.
• MLOps, full model lifecycle management, compliant AI design, and enterprise AI transformation leadership are preferred.
• AWS, Azure, or GCP AI/ML certifications are preferred.

Technical skills

• Required LLM ecosystems include GPT-4, Claude, LLaMA, and enterprise or open-source models; small language models include Phi-3, Gemma, and TinyLlama.
• Required agent frameworks include LangChain, LangGraph, Semantic Kernel, and Agno; RAG systems, vector databases, semantic retrieval, and hybrid retrieval are also listed.
• Fine-tuning methods include LoRA, QLoRA, DoRA, AdaLoRA, prompt tuning, adapters, prefix tuning, and P-tuning v2; alignment pipelines include RLHF and RLAIF.
• Model optimization includes quantization techniques such as INT8/INT4, GPTQ, AWQ, and GGML, as well as pruning and distillation.
• Platforms and tools include TensorFlow, PyTorch, Hugging Face, LlamaIndex, AWS, Azure, GCP, CI/CD, Docker, Kubernetes, monitoring, and observability.

Architecture and delivery

• Design distributed AI system architectures and produce scalable, high-quality production code.
• Benchmark and evaluate models, optimize cost and performance, and engineer security, privacy, and AI governance.
• Deploy and scale systems across cloud, edge, and on-premises environments.
• Build API-driven systems and full-stack AI services; implement autoscaling, rate limiting, and resource governance.
• Support real-time inference, streaming pipelines, and adaptive reasoning control.