Software Engineer

Job Description

Posted Tuesday, July 28, 2026, 7:30 PM Expires Wednesday, September 30, 2026, 7:29 PM

Who We Are

Magnit is the future of work. Serving hundreds of the world’s most recognizable brands for the past 30+ years, Magnit offers the industry’s first holistic platform for the modern workforce. Magnit’s integrated workforce management (IWM) platform supported by data, software, intelligence, and best-in-class services team is key to our clients’ success. It can adapt quickly to regional or industry economic shifts, and provides the speed, scale, flexibility, transparency, and expertise required to meet an organization’s contingent workforce management, talent strategy and broader organization goals. At Magnit, you’ll work with passionate colleagues who collaborate and deliver meaningful results that positively transform the largest companies around the globe.

Software Engineer – AI/ML (1–3 Years Experience)

About the Role

We are building next-generation AI-powered platforms that leverage Large Language Models (LLMs), Machine Learning (ML), NLP, and agentic workflows to automate critical business processes across multiple domains. As a Software Engineer, you will work within our AI/ML team to design, build, and ship production-grade AI features with moderate guidance, while mentoring junior engineers and contributing to architectural decisions. This role is suited for engineers with 1–3 years of hands-on experience in AI/ML who are ready to take greater ownership of features and systems.

Responsibilities

• Design, develop, and deploy AI/ML workflows and models with minimal supervision.
• Build and maintain robust APIs and backend services powering AI-driven applications.
• Train, fine-tune, and evaluate ML models for tasks such as classification, prediction, and ranking, and iterate based on performance metrics.
• Work hands-on with LLMs (OpenAI, Claude, Gemini) to build prompt engineering strategies, RAG pipelines, and agentic workflows for real business use cases.
• Own document processing pipelines involving OCR, data extraction, and ML-based validation, and improve their accuracy and reliability.
• Integrate AI models with external systems via well-designed APIs, handling error cases, scaling, and observability.
• Write clean, tested, and maintainable code, and participate in code reviews for peers and junior engineers.
• Actively participate in agile sprints, technical design discussions, and architecture reviews, contributing your own proposals.
• Troubleshoot and debug issues across the ML/AI stack, from data pipelines to model outputs to API integrations.
• Mentor associate/junior engineers and support their onboarding and skill development.
• Document processes, decisions, and learnings, and contribute to team-wide knowledge sharing and best practices.

Required Skills

• Strong proficiency in Python, with solid software engineering fundamentals (clean code, version control, testing).
• Practical experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn, including model training and evaluation.
• Hands-on experience with NLP techniques (text classification, summarization, embeddings, information extraction).
• Working experience with LLM-based development — prompt engineering, RAG architectures, or agentic frameworks (e.g., LangChain, LlamaIndex, or similar).
• Solid understanding of data structures, algorithms, and system design fundamentals.
• Experience building and consuming REST/GraphQL APIs, and integrating third-party or internal services.
• Working knowledge of databases (SQL/NoSQL) and data modeling for ML pipelines.
• Practical exposure to cloud platforms (AWS, Azure, or GCP) for deploying and scaling ML/AI workloads.
• Familiarity with MLOps practices — model versioning, monitoring, CI/CD for ML — is a plus.
• Exposure to containerization (Docker) and orchestration basics (Kubernetes) is a plus.

Mindset We Are Looking For

• Ownership: Comfortable owning features end-to-end, from design through deployment and monitoring.
• Problem-solving: Able to independently debug, experiment, and land solutions under ambiguity.
• Curiosity: Genuinely excited about GenAI, ML, and staying current with fast-evolving tools and techniques.
• Collaboration & mentorship: Works well with architects and seniors, while guiding junior team members.
• Pragmatism: Balances speed, quality, and scalability when shipping AI features to production.
• **Growth min…