AI Engineer

Role Overview

The AI Engineer is responsible for helping build, deploy and scale innovative AI-powered products and productivity solutions. This role is focused on turning business ideas into real-world applications using modern AI frameworks, Azure/AWS components, and XOps workflows.

This role works in close partnership with the AI Solutions Architect, software development engineering teams, and data engineering teams. The responsibilities of this role will center around (a) XOps engineering for AI (building, extending, optimizing pipelines and modules that effectively productionize AI solutions), and (b) helping build AI products based on existing (internally or externally created) models and components.

Key Responsibilities

• Build and deploy enterprise-ready AI solutions using LLMs, other GenAI approaches, and deep learning capabilities.
• Scale out AI solutions: optimize performance of solutions, automate deployment and testing
• Rapidly prototype and iterate on AI applications using Azure, AWS, and off-the-shelf tools.
• Partner with the AI Solutions Architect to ensure scalable, secure, and compliant system design.
• Develop APIs and lightweight UIs (e.g., Dash, Flask and others) to deliver AI tools to end users.
• Stay current on emerging AI technologies, including vector databases, RAG pipelines, and productivity AI platforms.
• Drive delivery of AI components aligned with product roadmaps and business priorities.

Core Technologies

• Data & Cloud: Azure, AWS, Google Cloud, Snowflake
• ML/AI Frameworks: PyTorch, MLflow, Hugging Face, LangChain, MCP
• UI & Deployment: Dash, Flask, FastAPI
• Databases: SQL, NoSQL, Graph Databases (Neo4J or others), Document Databases (MongoDB or others)
• MLOps: GitHub Actions, CI/CD, containerization, pipeline orchestration

Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field.

Work Experience

• 3+ years of hands-on experience in machine learning engineering or AI solution delivery.
• Experience working with Agile (SAFe) teams, iterative delivery cycles, and DevOps practices.
• Prior experience with deploying and scaling GenAI or ML-based solutions. Within clinical/pharmaceutical/CRO industries a plus.
• Experience working within a clinical regulated industry, and developing CRF Part11 compliant solutions is a plus.
• Strong experience with many of the technologies mentioned above under ‘Core Technologies’.

Skills & Competencies

• Strong software engineering fundamentals, with a focus on model observability, testing, and automation.
• Clear communicator who can distill technical detail for non-technical stakeholders.
• Agile mindset with comfort in fast-paced, iterative environments.
• Deep appreciation for data privacy, bias mitigation, and responsible AI practices.
• Commitment to ethical AI development and AI governance principles.