AI Applied Engineer - Private Equity Focus

TechTorch is a high-growth enterprise technology consultancy that partners with private equity-backed businesses. The AI Applied Engineer will design and build full-stack software systems, data platforms, and AI-powered MVPs for clients, working end-to-end from frontend development to AI model integration.

Responsibilities:

• Design and build full-stack applications, including:
• Frontend interfaces using React and Next.js
• Backend services and APIs using Python and cloud-native patterns
• Develop secure, scalable application logic that integrates with data platforms and enterprise systems
• Implement CI/CD pipelines, testing, and production-readiness standards
• Collaborate closely with product, data, and AI teams to deliver cohesive user experiences
• Design, build, and maintain scalable data pipelines and ELT workflows
• Model and manage data in modern data platforms (e.g., Snowflake, Redshift, BigQuery, lakehouse architectures)
• Ensure data quality, performance, and lineage across structured and semi-structured data
• Enable analytics, reporting, and downstream AI use cases through well-designed data foundations
• Build AI prototypes and MVPs end-to-end for clients — from data ingestion to user-facing experience
• Integrate AI/ML capabilities into software and data workflows (e.g., NLP, document intelligence, classification, anomaly detection)
• Leverage modern AI platforms and services such as AWS Bedrock, SageMaker, Snowflake Cortex, OpenAI
• Support feature engineering, inference pipelines, and AI-enabled automation
• Ensure AI solutions are observable, explainable, and suitable for enterprise and regulated environments

Requirements:

• 5+ years of experience across full-stack software engineering and data engineering
• Strong foundation in custom software development, including: Frontend: React, Next.js
• Backend: Python, strong API and service design
• Solid hands-on experience with data engineering: pipelines, modeling, warehouses, ELT/ETL
• Practical experience building and deploying AI-powered prototypes or MVPs, not just experimentation
• Proficient in Python, SQL, and modern cloud-native development practices
• Experience with cloud platforms (AWS preferred; Azure or GCP acceptable)
• Comfortable operating across software, data, and AI — able to wear multiple hats
• Strong problem-solving skills and ability to translate business needs into technical solutions
• Consulting or client-facing experience is a strong plus
• Snowflake (including Cortex AI), dbt, Airflow, Spark
• AWS services: Lambda, Step Functions, Bedrock, SageMaker
• Exposure to MLOps, model lifecycle management, or AI governance
• Experience in regulated or data-sensitive environments