Chief Engineer - Artificial Intelligence

AI Architecture | Production Engineering | LLM, RAG & Agentic AI

Primary objective: Build secure, scalable and reusable AI capabilities that move from experimentation to reliable production use across DocTutorials products and business functions.

Department

Technology & Product Engineering

Location

Hyderabad / Chennai

Reports to

Vice President - Technology

Experience

10+ years in software engineering, including 5+ years in AI/ML

Role Summary

The Chief Engineer - AI will define and lead AI engineering architecture, standards and production implementation. This is a senior, hands-on technical leadership role responsible for building reliable AI platforms and guiding AI engineers, data teams and application teams.

Key Responsibilities

1. AI Strategy and Architecture

· Define the AI engineering strategy, architecture and technical roadmap.

· Evaluate and select suitable models, frameworks, vector databases and AI platforms.

· Make practical build-versus-buy and open-source-versus-commercial decisions.

· Ensure AI solutions are modular, reusable, scalable, secure and cost-effective.

2. Production AI Engineering

· Lead AI initiatives from use-case assessment and proof of concept through production deployment.

· Build reusable AI services, APIs, pipelines and platform components for web, mobile and internal applications.

· Establish development, testing, deployment, monitoring and production-support practices.

· Resolve complex issues involving model quality, retrieval, performance, reliability and cost.

3. LLM, RAG and Agentic AI

· Design LLM applications, enterprise RAG solutions and controlled AI-agent workflows.

· Build document processing, chunking, embedding, indexing, retrieval and response pipelines.

· Implement source traceability, human approval and explicit permissions for sensitive use cases.

· Improve response quality through prompt design,



retrieval optimisation and evaluation.

4. AI Quality, Security and Responsible AI

· Define evaluation standards for accuracy, relevance, groundedness, safety, latency and cost.

· Introduce benchmark datasets and regression testing for prompts, models and retrieval pipelines.

· Implement safeguards for hallucinations, unsupported responses, data leakage and unsafe actions.

· Ensure role-based access, encryption, audit logging, secure secrets handling and approved data usage.

5. AI Platform and Integration

· Create common AI capabilities and integration standards that can be reused across products and teams.

· Integrate AI services with applications, databases and approved enterprise data sources.

· Implement observability for model usage, quality, errors, response time and cost.

· Reduce dependency on a single model provider, individual engineer or external vendor.

6. Technical Leadership and Team Development

· Define AI engineering standards for architecture, coding, review, testing and documentation.

· Remain hands-on through design reviews, critical code reviews, debugging and focused prototypes.

· Mentor AI/ML engineers, data engineers and application teams; support hiring and capability building.

· Work with Product and business teams to convert high-value problems into measurable AI solutions.

Required Skills and Experience

· 10+ years of software engineering experience, including 5+ years in practical AI/ML engineering.

· Proven experience moving AI solutions from prototype to reliable production use.





· Strong Python and production API development experience, preferably FastAPI.

· Strong knowledge of LLMs, RAG, prompt engineering, embeddings and agentic AI.

· Experience with vector databases such as pgvector, Qdrant, Milvus or Weaviate.

· Experience with LangChain, LlamaIndex, Haystack or similar orchestration frameworks.

· Knowledge of cloud architecture (preferably AWS), microservices, databases, queues and distributed systems.

· Experience with Docker, Kubernetes, CI/CD, observability and production support.

· Strong understanding of application security, data privacy and responsible-AI engineering.

· Demonstrated technical leadership, mentoring and stakeholder-management capability.

Good-to-Have Experience

· Healthcare, medical education, EdTech or digital-learning platforms.

· Medical-content intelligence, knowledge graphs, ontologies, taxonomy or semantic search.

· Clinical Case Library, personalised learning or recommendation systems.

· Multi-modal AI involving text, audio, images or video.

· Healthcare-data privacy, de-identification or standards such as SNOMED CT, ICD, HL7 or FHIR.

How Success Will Be Measured

· Production adoption and measurable business or learner impact of AI capabilities.

· Accuracy, groundedness, reliability, security and cost efficiency of AI solutions.

· Reusable AI services delivered across products with reduced technical and vendor dependency.

· Improved capability, engineering quality and ownership within the AI team.

We are looking for a hands-on AI engineering leader who can distinguish a valuable production capability from an attractive demo and build AI that is secure, scalable, measurable and maintainable.

Skills:- Artificial Intelligence (AI), Machine Learning (ML), Python, Large Language Models (LLM) tuning and Retrieval Augmented Generation (RAG)

📌 Chief Engineer - Artificial Intelligence (Hyderabad)
🏢 MindBridge
📍 Hyderabad