Description

Key Responsibilities

• Design, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems.

• Design and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents).

• Build retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking.

• Develop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate.

• Apply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics.

• Build and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility.

• Productionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies.

• Use AI coding assistants (Claude Code, Cursor) effectively alongside traditional manual coding — choosing the right approach for each task and reviewing AI-generated code with rigor.

• Manage source code in Git using a clean branching strategy and pull-request-based review.

• Build and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services.

• Deploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep).

• Instrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time.

• Partner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts.

• Work directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job.

• Mentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems.

• Conduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first-class concerns.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field — or equivalent practical experience.

• 4 + years of professional software engineering experience, with at least 2 years building and shipping ML or AI systems in production.

• Strong proficiency in Python and core ML libraries (PyTorch and/or TensorFlow, scikit-learn, pandas, NumPy).

• Hands-on experience deploying LLM-based applications using major model providers (Anthropic Claude, Azure OpenAI, OpenAI, or open-source models).

• Practical RAG and embeddings experience, including at least one production vector store.

• Demonstrated proficiency with AI coding tools such as Claude Code and Cursor, balanced with strong manual coding fundamentals.

• Solid command of Git, branching strategies, and pull-request-based code review.

• Hands-on experience building and operating CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for production workloads.

• Hands-on experience deploying services on Azure (AKS, App Service, Functions, Azure AI/ML services, or similar) and with containerization (Docker).

• Strong written and verbal communication; able to explain Modeling decisions and trade-offs to non-technical stakeholders.

• Proven ability to thrive in a fully remote, globally distributed team.

Requirements added by the job poster

• 2+ years of work experience with Large Language Models (LLM)

• 3+ years of work experience with Microsoft Azure

• 3+ years of work experience with Python (Programming Language)

Key Skills

Education

Bachelor's or Master's degrees