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 mindset: Continuously sharpens technical depth in AI/ML while broadening system-level thinking.