Lead/ Architect AI Engineer (LLM / GenAI / MLOps)

Qualifications

• 8–10 years of experience in Machine Learning / Software Engineering

• 4+ years deploying deep learning models in production

• Strong expertise in Python, PyTorch or TensorFlow

• Hands-on experience with LLM ecosystem:

• LangChain / LlamaIndex

• Vector DBs (Pinecone, Weaviate)

• Inference optimization (vLLM, quantization)

• Strong background in ML & data processing (scikit-learn, XGBoost, Spark, Ray)

• Experience with MLOps & Cloud (AWS / GCP / Azure, Docker, Kubernetes, MLflow, FastAPI)

• 6 more items(s)

Responsibilities

• Design and scale ML pipelines and Generative AI applications

• Lead development of LLM-based solutions using RAG, prompt engineering, and fine-tuning (LoRA/PEFT)

• Train and optimize machine learning models, classifiers, and recommendation systems

• Oversee MLOps lifecycle including CI/CD, deployment, monitoring, and drift detection

• Ensure AI safety, governance, and performance optimization

• Mentor junior engineers and drive AI best practices

• 3 more items(s)

More job highlights

Job description

Job Title: Lead/ Architect AI Engineer (LLM / GenAI / MLOps)

Location: San Francisco, CA (Onsite)

Job Description:

We are seeking a highly experienced AI Engineer / Technical Leader to architect and scale advanced AI systems, combining traditional machine learning with cutting-edge Generative AI and Large Language Models (LLMs).

Key Responsibilities:

• Design and scale ML pipelines and Generat...ive AI applications

• Lead development of LLM-based solutions using RAG, prompt engineering, and fine-tuning (LoRA/PEFT)

• Train and optimize machine learning models, classifiers, and recommendation systems

• Oversee MLOps lifecycle including CI/CD, deployment, monitoring, and drift detection

• Ensure AI safety, governance, and performance optimization

• Mentor junior engineers and drive AI best practices

Required Skills:

• 8–10 years of experience in Machine Learning / Software Engineering

• 4+ years deploying deep learning models in production

• Strong expertise in Python, PyTorch or TensorFlow

• Hands-on experience with LLM ecosystem:

• LangChain / LlamaIndex

• Vector DBs (Pinecone, Weaviate)

• Inference optimization (vLLM, quantization)

• Strong background in ML & data processing (scikit-learn, XGBoost, Spark, Ray)

• Experience with MLOps & Cloud (AWS / GCP / Azure, Docker, Kubernetes, MLflow, FastAPI)

Preferred Qualifications:

• Experience with multi-modal AI models

• Contributions to open-source AI projects

• Knowledge of AI guardrails and governance frameworks

• Advanced degree (Master’s or Ph.D.) in CS, AI, Math, or related field

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