AI Engineer III

Requirements:

• 6 - 9 years of experience.
• Strong Python programming skills.
• Hands-on LLM development.
• Solid understanding of NLP fundamentals and Transformer architectures.
• Fine-tuning model building & model building experience.
• Strong Computer Science and Machine Learning fundamentals.
• Top Campuses (IITs, IISc, IIITs, BITS, top NITs).
• Should Have Advanced LLM knowledge, AI Infrastructure Evaluation & AI quality.
• Could Have Published research or patents in AI/ML, open-source contributions, Kaggle/competitive ML achievements.
• Experience building foundation models, Reinforcement Learning experience, vision-language models (VLMs), speech AI, multimodal AI.
• Domain expertise in Healthcare AI, experience with Knowledge Graphs.
• Experience serving billion-token or high-scale inference workloads.
• Won't Have Prompt engineering without strong ML understanding; ChatGPT wrapper projects with limited technical depth Low-code/no-code AI development.
• Primarily data analyst or BI backgrounds.
• Research-only profiles with no production engineering experience; MLOps-only or Platform-only engineers without LLM expertise.
• Backend engineers who have only integrated OpenAI APIs without understanding underlying AI concepts.
• Candidates unable to explain architectural decisions, model behaviour, or engineering trade-offs.
• Candidates whose experience is limited to demos, hackathons, or proofs-of-concept without shipping production AI systems.

What Great Looks Like:

An ideal AI Engineer III has:

• 6-10 years of experience building production AI systems, possesses deep software engineering and machine learning fundamentals, understands modern LLM architectures beyond API usage, has hands-on experience training or adapting models, and has successfully delivered scalable, reliable AI platforms from concept to production.
• They combine strong technical depth with ownership, architectural thinking, and the ability to mentor others while remaining highly hands-on.