We are seeking a Full Stack Software Engineer to design, build, and scale AI-enabled products that integrate Large Language Models (LLMs) into core business workflows. This role is focused on end-to-end product development-from frontend experiences to backend services and AI integrations-delivering secure, scalable, and production-grade solutions.
Requirements
- 7+ years of experience designing and developing distributed application architecture of moderate-to-high complexity.
- 3+ years in software engineering or applied ML building real-world AI/ML systems; strong Python proficiency and backend development expertise
- Hands-on experience building GenAI apps with LangChain and LangGraph, including agent design, state/memory management, and graph-based orchestration.
- Proficiency in ML/NLP and generative models; experience with embeddings, vector stores, RAG, and LLM integration/fine-tuning (OpenAI, LLaMA, Cohere, etc.)
- Strong coding in Python and experience with frameworks/tools such as FastAPI, PyTorch/TensorFlow, MLflow;
- 3-5+ years of experience in designing and developing scalable web applications using modern front-end frameworks such as React/TypeScript.
- Handsāon experience with modern data platforms and cloud environments (e.g., Snowflake, Databricks, AWS and/or Azure).
- Experience building and operating data pipelines, including batch and streaming patterns, with orchestration tools such as Airflow, ADF, or Dagster.
- Experience working in high-performance teams using Agile methodologies.
- Experience with CI/CD concepts and implementing build and deployment pipelines incorporating Security, Automation and Quality (DevSecOps).
- Familiarity with modern data architecture and engineering technologies
- Excellent communication skills with ability to articulate ideas clearly and concisely.
Benefits
- Opportunity to work on AI-first product development, embedding LLM capabilities into real-world applications
- Ownership of full-stack delivery in a modern, cloud-native engineering environment
- Collaboration with senior engineers, architects, and AI platform teams
- Exposure to internal AI platforms, agentic frameworks, and GenAI enablement initiatives
- Strong engineering culture emphasizing design quality, scalability, and operational excellence
