Senior AI/ML Engineer

Overview

We are seeking a highly motivated Senior AI/ML Engineer with 4+ years of experience building, fine-tuning, and deploying large language models (LLMs) in production. This role is focused on designing scalable, high-performance ML systems, improving inference efficiency, and delivering reliable, production-grade AI solutions.

You will work across the full ML lifecycle—from distributed GPU training to cloud deployment—, optimizing cost and performance, and collaborating cross-functionally to deliver impactful AI products.

Key Responsibilities

• Design, build, and deploy scalable AI/ML systems for production environments

• Optimize LLM performance across latency, throughput, memory usage, and cost

• Architect end-to-end ML systems, making tradeoffs across performance, scalability, and reliability

• Develop, fine-tune, and evaluate LLMs and deep learning models

• Implement advanced prompt engineering strategies to improve output quality, consistency, and reliability

• Build and optimize retrieval-augmented generation (RAG) systems, including integration with vector databases

• Apply model optimization techniques such as quantization, pruning, batching, and efficient inference strategies

• Deploy and maintain production-grade APIs and model endpoints (e.g., FastAPI)

• Design and maintain distributed data pipelines and cloud-based ML infrastructure

• Build and maintain MLOps pipelines, including experiment tracking, model versioning, and CI/CD workflows

• Implement monitoring, logging, and alerting systems for model performance, drift detection, and system reliability

• Develop robust evaluation frameworks, including offline evaluation, online testing, and A/B experimentation

• Implement safety, alignment, and guardrail mechanisms to mitigate hallucinations, bias, and unsafe outputs

• Optimize infrastructure and deployment strategies for cost efficiency

• Partner with product, engineering, and leadership teams to translate business requirements into scalable AI solutions

• Stay current with emerging research, tools, and best practices in AI/ML

Required Qualifications

• Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field

• 4+ years of hands-on experience building and deploying ML/LLM systems in production

• Strong proficiency in Python (required) and experience with C++ (preferred)

• Deep experience with ML frameworks such as PyTorch and/or TensorFlow

• Strong understanding of NLP, LLMs, and deep learning architectures

• Proven experience optimizing models for production, including GPU acceleration and efficient inference

• Hands-on experience with distributed training

• Experience deploying models at scale with AWS or Google Cloud

• Experience building APIs using FastAPI

• Strong experience with Linux and scripting

• Proficiency with Git

• Solid understanding of databases (PostgreSQL, MySQL)

Nice to Have

• Experience with TensorRT-LLM, vLLM, or DeepSpeed

• Experience with LangChain or LlamaIndex

• Experience with OpenAI, Anthropic, or open-weight models

• Familiarity with MLflow, Weights & Biases, or Kubeflow

• Experience with LLM evaluation frameworks

• Experience with RLHF or DPO

• Experience with multimodal models

• Contributions to open-source or research publications

What Success Looks Like

• Deliver scalable LLM systems in production

• Reduce latency and infrastructure costs while maintaining quality

• Build reliable systems with strong monitoring and safety

• Contribute to scalable architecture decisions

• Drive measurable improvements in model performance

Pay: $175,000.00 - $230,000.00 per year

Work Location: In person