Chief Engineer

Chief Engineer Artificial Intelligence

Location: Hyderabad
Experience: 10+ years in software engineering, including 5+ years in AI/ML
Employment Type: Full-time

Job Summary

We are looking for a hands-on AI engineering leader to define AI architecture, establish engineering standards, and lead the development of secure, scalable, and production-ready AI solutions. The role involves building reusable AI capabilities, leading LLM/RAG and agentic AI initiatives, and ensuring reliable integration of AI services across digital products and business functions.

Key Responsibilities

AI Strategy and Architecture

• Define AI engineering strategy, architecture, and technical roadmaps.
• Evaluate and select suitable AI models, frameworks, vector databases, and platforms.
• Make build-versus-buy and open-source-versus-commercial technology decisions.
• Design modular, reusable, scalable, secure, and cost-effective AI solutions.

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.
• Troubleshoot complex challenges involving model quality, retrieval, performance, reliability, and cost.

LLM, RAG and Agentic AI

• Design LLM applications, enterprise RAG solutions, and controlled AI-agent workflows.
• Develop document processing, chunking, embedding, indexing, retrieval, and response-generation pipelines.
• Implement source traceability, human approval, and appropriate permissions for sensitive use cases.
• Improve response quality through prompt engineering, retrieval optimization, and systematic evaluation.

AI Quality, Security and Responsible AI

• Define evaluation standards for accuracy, relevance, groundedness, safety, latency, and cost.
• Develop benchmark datasets and regression testing for prompts, models, and retrieval pipelines.
• Implement safeguards against hallucinations, unsupported responses, data leakage, and unsafe actions.
• Ensure role-based access, encryption, audit logging, secure secrets management, and approved data usage.

AI Platform and Integration

• Develop reusable AI capabilities and integration standards across products and teams.
• Integrate AI services with applications, databases, and approved enterprise data sources.
• Implement observability for model usage, quality, errors, response times, and costs.
• Reduce dependency on individual model providers, engineers, and external vendors.

Technical Leadership

• Establish engineering standards for architecture, coding, reviews, testing, and documentation.
• Remain hands-on through design reviews, critical code reviews, debugging, and prototypes.
• Mentor AI/ML engineers, data engineers, and application teams.
• Support technical hiring and capability development.
• Collaborate with Product and business teams to convert business 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 taking AI solutions from prototype to reliable production deployment.
• Strong Python and production API development skills, 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.
• Proven technical leadership, mentoring, and stakeholder-management skills.

Good-to-Have Skills

• Healthcare, medical education, EdTech, or digital-learning platform experience.
• Medical-content intelligence, knowledge graphs, ontologies, taxonomy, or semantic search.
• Clinical case libraries, personalized learning, or recommendation systems.
• Multimodal AI involving text, audio, images, or video.
• Healthcare-data privacy, de-identification, or standards such as SNOMED CT, ICD, HL7, or FHIR.

Key Success Metrics

• Production adoption and measurable business impact of AI capabilities.
• Improved accuracy, groundedness, reliability, security, and cost efficiency.
• Reusable AI services adopted across products with reduced technical and vendor dependency.
• Improved engineering quality, team capability, and technical ownership.

Ideal Candidate

A hands-on AI engineering leader who can distinguish production-ready capabilities from experimental demos and build secure, scalable, measurable, and maintainable AI systems.