Job Description

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