Software AI Engineer - US

Jade Global • San Jose, CA

11h • Hybrid

About The Position

Software AI Engineer - US1 Job Title: Software AI Engineer/Architect Location: Santa Clara, CA (onsite preferred but remote candidates can be considered) Experience: 8- 10 yrs Job Type: Contract/ FTE This role requires deep, end-to-end understanding of how Large Language Models are built, trained, optimized, deployed, and operated. Candidates must demonstrate hands-on experience beyond consuming hosted LLM APIs, with a strong grasp of the underlying ML theory, system trade-offs, and production realities of AI/ML solutions. Mandatory Competency Areas (Non-Negotiable) 1. Foundations of LLMs (How They Actually Work) Candidate must demonstrate first-principles understanding, including: Transformer architectures (attention, embeddings, positional encoding) Tokenization strategies and their impact on cost & performance Training vs inference behavior Loss functions, pre-training objectives, and alignment techniques (SFT, RLHF) Limitations: hallucinations, bias, context collapse, long-range degradation 2. Model Development & Adaptation Hands-on experience with: Pre-training vs fine-tuning trade-offs Parameter-efficient tuning (LoRA, QLoRA, adapters) Quantization and pruning techniques Model evaluation beyond accuracy (task fitness, safety, robustness) Data curation, labeling strategies, and contamination risks. Model Development & Adaptation 3. Inference, Serving & Optimization Strong understanding of: Inference pipelines and token generation mechanics KV caching, batching, streaming responses Throughput vs latency trade-offs Memory constraints and GPU utilization strategies Model parallelism (tensor, pipeline) and their failure modes 4. End-to-End AI/ML System Design Ability to architect complete AI solutions, including: Data ingestion and preprocessing pipelines Training / fine-tuning workflows Model registry, versioning, and lineage Deployment strategies (canary, A/B, shadow traffic) Feedback loops for continuous improvement 5. Retrieval, Memory & Tool-Augmented Systems In-depth experience with: Retrieval-Augmented Generation (RAG) design Embeddings lifecycle management Vector databases and hybrid retrieval Prompt/tool orchestration and agentic workflows Failure modes of RAG and mitigation strategies 6. MLOps, Observability & Reliability Strong ownership mindset for production AI: Monitoring model quality drift and regressions Debugging hallucinations and retrieval failures Logging prompts, responses, and model metadata Cost tracking and optimization (token economics) Incident response for AI systems 7. Security, Ethics & Governance Clear understanding of: Prompt injection and data leakage risks Training data privacy and IP protection Model abuse, misuse, and guardrails Regulatory and compliance considerations Responsible AI principles in production systems

Requirements

• Deep, end-to-end understanding of how Large Language Models are built, trained, optimized, deployed, and operated.
• Hands-on experience beyond consuming hosted LLM APIs, with a strong grasp of the underlying ML theory, system trade-offs, and production realities of AI/ML solutions.
• Foundations of LLMs (How They Actually Work): Candidate must demonstrate first-principles understanding, including: Transformer architectures (attention, embeddings, positional encoding), Tokenization strategies and their impact on cost & performance, Training vs inference behavior, Loss functions, pre-training objectives, and alignment techniques (SFT, RLHF), Limitations: hallucinations, bias, context collapse, long-range degradation
• Model Development & Adaptation: Hands-on experience with: Pre-training vs fine-tuning trade-offs, Parameter-efficient tuning (LoRA, QLoRA, adapters), Quantization and pruning techniques, Model evaluation beyond accuracy (task fitness, safety, robustness), Data curation, labeling strategies, and contamination risks.
• Inference, Serving & Optimization: Strong understanding of: Inference pipelines and token generation mechanics, KV caching, batching, streaming responses, Throughput vs latency trade-offs, Memory constraints and GPU utilization strategies, Model parallelism (tensor, pipeline) and their failure modes
• End-to-End AI/ML System Design: Ability to architect complete AI solutions, including: Data ingestion and preprocessing pipelines, Training / fine-tuning workflows, Model registry, versioning, and lineage, Deployment strategies (canary, A/B, shadow traffic), Feedback loops for continuous improvement
• Retrieval, Memory & Tool-Augmented Systems: In-depth experience with: Retrieval-Augmented Generation (RAG) design, Embeddings lifecycle management, Vector databases and hybrid retrieval, Prompt/tool orchestration and agentic workflows, Failure modes of RAG and mitigation strategies
• MLOps, Observability & Reliability: Strong ownership mindset for production AI: Monitoring model quality drift and regressions, Debugging hallucinations and retrieval failures, Logging prompts, responses, and model metadata, Cost tracking and optimization (token economics), Incident response for AI systems
• Security, Ethics & Governance: Clear understanding of: Prompt injection and data leakage risks, Training data privacy and IP protection, Model abuse, misuse, and guardrails, Regulatory and compliance considerations, Responsible AI principles in production systems

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed