Senior Full Stack ML Engineer

Full-Stack ML Engineer

About the Role

We are seeking a highly skilled Senior Full-Stack ML engineer/ Senior Data Scientist with expertise in Generative AI and Large Language Models (LLMs) who can bridge the gap between advanced AI research and practical enterprise-grade solutions. The ideal candidate will not only build intelligent ML/AI models but also design scalable microservices, integrate back-end and front-end systems, and own deployment on cloud platforms.

Key Responsibilities

• AI/ML Development
  • Research, develop, and optimize Generative AI/LLM-based solutions for business use cases (e.g., retrieval-augmented generation, fine-tuning, embeddings).
  • Experiment with techniques like LoRA, QLoRA, PEFT, and prompt engineering for efficient model customization.
  • Collaborate with data engineering teams on feature pipelines and real-time inference.
• System Design & Engineering
  • Design and develop scalable microservices for model serving and inference.
  • Build APIs that expose AI capabilities for enterprise application integration.
  • Optimize system performance for large-scale AI workloads.
• Full-Stack Integration
  • Work with back-end frameworks (Python, Node.js, FastAPI, Django, Flask, etc.) for model orchestration.
  • Collaborate with front-end developers to design intuitive interfaces for Gen AI applications (React, Angular, or Streamlit).
  • Ensure seamless end-to-end integration between data pipelines, models, APIs, and UI.
• Cloud & Deployment
  • Deploy and manage AI/ML workloads on cloud platforms (AWS, Azure, GCP, or Snowflake AI).
  • Containerize applications using Docker and orchestrate services using Kubernetes.
  • Implement CI/CD pipelines for automation, monitoring, and scaling of ML services.
• Collaboration & Leadership
  • Mentor junior data scientists and engineers.
  • Partner with business stakeholders to translate requirements into production-ready AI features.
  • Ensure compliance, data security, and best practices in responsible AI.

Required Skills & Qualifications

• 7+ years of experience in applied machine learning, with 2+ years in Generative AI/LLMs.
• Proficiency in Python (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex).
• Strong understanding of full-stack development (back end + front end).
• Hands-on exposure to microservices architecture, APIs, and distributed systems.
• Practical experience with cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI, or Snowflake machine learning).
• Knowledge of DevOps/MLOps practices including Docker, Kubernetes, and CI/CD tools.
• Excellent problem-solving, communication, and leadership skills.