NLP Full Stack Data Scientist

As an AI/ML Engineer, you will work across the full software development lifecycle—from exploring emerging AI technologies and building prototypes to designing scalable architectures and deploying production-grade applications.

You will develop LLM-powered features, including retrieval-augmented generation (RAG), agentic workflows, prompt orchestration, and model evaluation frameworks. You will also contribute to frontend and backend applications using React and Python, while leveraging cloud platforms to build secure, scalable, and observable systems.

The ideal candidate is both an AI practitioner and a hands-on software engineer who can rapidly transform business problems into practical solutions using modern AI tools, robust design patterns, and engineering best practices.

Responsibilities

• Design, build, and deploy AI/ML-powered applications from concept through production.
• Build scalable backend services and APIs using Python and frameworks such as FastAPI, Flask, or Django.
• Develop responsive and user-friendly frontend applications using React, JavaScript, and TypeScript.
• Design scalable software architecture, reusable components, and appropriate design patterns for AI and non-AI applications.
• Productionize and deploy AI models and inference services using cloud platforms such as AWS, Azure, or GCP.
• Build and maintain MLOps and CI/CD pipelines for application code, models, prompts, and evaluation assets.
• Implement observability using logs, metrics, traces, dashboards, and alerts.
• Define and monitor service-level objectives for latency, availability, accuracy, error rate, and cost.
• Use AI-assisted software development tools to accelerate coding, testing, documentation, and prototyping while maintaining engineering quality.
• Collaborate with product managers, domain experts, data scientists, and software engineers to convert business needs into measurable AI quality goals.
• Participate in architecture reviews, technical design discussions, and production readiness assessments.
• Mentor engineers on AI/ML engineering practices, software architecture, testing, and responsible use of AI tools.
• Stay current with advancements in generative AI, machine learning, cloud technologies, and software engineering practices.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
• Experience developing and deploying machine learning or AI-powered applications.
• Strong programming experience in Python.
• Experience building production APIs and backend services using FastAPI, Flask, Django, or similar frameworks.