AI Software Engineer

Primary objective: Build, test and operate AI services and pipelines for DocTutorials under defined engineering and responsible-AI standards.

The AI Software Engineer will implement practical LLM, RAG and AI automation capabilities and work with senior engineers, Product and application teams to deliver production-ready outcomes.

Key Responsibilities

1. AI Development

- Develop AI services and APIs using Python and FastAPI.
- Implement document processing, embeddings, vector search, retrieval and prompt workflows.
- Integrate approved LLMs and AI services with DocTutorials applications.
- Build tests, error handling and monitoring for AI pipelines.

1. Quality and Responsible AI

- Evaluate outputs for relevance, groundedness, consistency, safety and latency.
- Maintain source traceability and human-review controls where required.
- Protect learner, content and organisational data throughout development.
- Track experiments, prompts, models, datasets and configuration changes.

1. Collaboration and Growth

- Work with application engineers, QA, Product and domain experts.
- Participate in design reviews, code reviews and incident analysis.
- Learn production AI patterns, MLOps/LLMOps and cost optimisation.
- Take ownership of assigned AI components through production support.

Required Skills and Experience
- 2+ years of software,



data or AI engineering experience.
- Robust Python fundamentals and API-development exposure, preferably FastAPI.
- Hands-on knowledge of LLMs, RAG, embeddings and vector databases.
- Basic experience with LangChain, LlamaIndex, Haystack or similar frameworks.
- SQL, Git, testing, Docker and cloud fundamentals.
- Practical understanding of Generative AI, LLMs, RAG, Agentic AI and responsible AI use relevant to the role.
- Ability to use approved AI tools productively while protecting source code, credentials, learner data and organisational information.
- Commitment to human review, testing, security validation and traceability for AI-assisted outputs.

Good-to-Have Experience
- Machine learning, NLP, model serving or fine-tuning exposure.
- AWS, Kubernetes, MLOps/LLMOps or observability.
- EdTech or medical-learning experience.

How Success Will Be Measured
- Assigned AI services function reliably in production.
- Evaluation and testing identify quality issues early.
- Data and security standards are followed consistently.
- Technical independence and AI engineering depth improve.
- Work consistently delivers measurable outcomes

Expected mindset: Own the outcome, use AI responsibly, communicate risks early and deliver secure, measurable value for DocTutorials and its learners.

📌 AI Software Engineer (Hyderabad)
🏢 DocTutorials Edutech
📍 Hyderabad