Senior AI Engineer

Job Summary 👩‍💻

We are seeking a Senior AI Engineer to design, build, and deploy production-grade artificial intelligence (AI) and machine learning (ML) systems that drive measurable business impact. You will work alongside cross-functional teams — including data scientists, software engineers, and product managers — to translate research prototypes into scalable, reliable AI solutions. As a Senior AI Engineer, you will lead the architecture of ML pipelines, own model lifecycle management from development through deployment, and establish engineering best practices for AI systems across the organization. This role requires deep expertise in large language models (LLMs), machine learning frameworks, and MLOps tooling, combined with the software engineering rigor to deliver AI products at scale.

Key Responsibilities 🧠

Design and implement end-to-end machine learning pipelines, from data ingestion and feature engineering through model training, evaluation, and deployment.

Lead the integration of LLMs and generative AI capabilities into production applications, including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) architectures.

Architect scalable, maintainable AI inference services and model-serving infrastructure using modern MLOps practices.

Collaborate with data scientists to translate experimental models into production-ready systems with monitoring, versioning, and continuous evaluation.

Define and enforce engineering standards for AI codebases, including code review, testing strategies, and documentation practices.

Partner with product and engineering teams to scope AI features, estimate complexity, and drive delivery from design to production.

Monitor deployed models for data drift, performance degradation, and fairness issues, and implement mitigation strategies.

Mentor mid-level and junior engineers on ML engineering best practices, system design, and production readiness.

Required Qualifications 🥸

5+ years of professional software engineering experience, with at least 3 years focused on machine learning engineering or applied AI in production environments.

Proven track record designing and shipping ML systems end-to-end in production, including data pipelines, model training, and serving infrastructure.

Deep proficiency in Python and at least one major ML framework (PyTorch, TensorFlow, or JAX).

Hands-on experience with LLMs, including fine-tuning, prompt engineering, and building applications on top of foundation models (e.g., OpenAI, Anthropic Claude, or open-source equivalents).

Experience with MLOps platforms and tools for experiment tracking, model registry, and CI/CD for machine learning (e.g., MLflow, Weights & Biases, Kubeflow, or equivalent).

Proficiency with at least one major cloud platform (AWS, GCP, or Azure) for deploying and scaling AI workloads.

Strong software engineering fundamentals: system design, API design, testing, and version control with Git.

Preferred Qualifications

Experience with RAG pipelines, vector databases (e.g., Pinecone, Weaviate, pgvector), and semantic search systems.

Familiarity with containerization and orchestration (Docker, Kubernetes) for AI workload deployment.

Background in distributed training, model optimization (quantization, distillation, ONNX export), or GPU infrastructure management.

Contributions to open-source ML projects or publication of AI research or technical writing.

Experience with agentic AI frameworks such as LangChain, LlamaIndex, AutoGen, or similar.

Exposure to responsible AI practices: fairness evaluation, explainability tooling, or bias mitigation.

Technical Skills Required

Category

Technologies

Languages

Python, SQL; TypeScript or Go a plus

ML Frameworks

PyTorch, TensorFlow, JAX, Hugging Face Transformers

LLM & Generative AI

OpenAI API, Anthropic Claude API, LangChain, LlamaIndex, RAG pipelines

MLOps & Experimentation

MLflow, Weights & Biases, DVC, Kubeflow, Apache Airflow

Cloud & Infrastructure

AWS (SageMaker, Lambda, S3), GCP (Vertex AI), or Azure ML

Serving & Deployment

FastAPI, TorchServe, Triton Inference Server, Docker, Kubernetes

Data & Pipelines

Apache Spark, dbt, Apache Kafka, or equivalent orchestration tools

Vector Databases

Pinecone, Weaviate, Chroma, pgvector

Version Control & CI/CD

Git, GitHub Actions, GitLab CI, or equivalent

Soft Skills Required

Ability to communicate complex AI concepts clearly to non-technical stakeholders, including product managers and business leaders.

Strong analytical and problem-solving mindset with a bias toward pragmatic, production-ready solutions over theoretical elegance.

Demonstrated ability to lead technical initiatives, influence architectural decisions, and build consensus across engineering teams.

High ownership and accountability — comfortable driving ambiguous projects from problem definition through delivery.

Effective collaboration skills in cross-functional environments, balancing delivery speed with engineering rigor.

Commitment to continuous learning in a rapidly evolving AI landscape.

Meticulous attention to code quality, reproducibility, and documentation in ML systems.

Education Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field — or equivalent professional experience. An advanced degree (M.S. or Ph.D.) in Artificial Intelligence, Machine Learning, or a related discipline is a plus but not required.