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Lead AI Engineer

IndiGo

Gurugram

8+ years

Today

$65.1K–80.7K/yr

Full time

Onsite

Skills Required

LLM

RAG

Prompt Engineering

Azure OpenAI

OpenAI APIs

Hugging Face

LangChain

LlamaIndex

LLMOps agents

MLOps

Semantic Kernel

Azure AI Search

Pinecone

Weaviate

Description

IndiGo is hiring a hands-on Lead Engineer - AI to design, build, and deliver scalable AI-powered applications and platforms. The role combines technical leadership, hands-on development, and mentoring across GenAI, MLOps, and cloud-native delivery.

Company: IndiGo

Role: Lead AI Engineer

Location: Gurgaon

Experience

- 8+ years of software engineering experience
- Experience in a technical leadership role
- Strong hands-on experience with Python and backend/API development
- Practical experience building AI/GenAI solutions using LLMs, RAG, embeddings, vector databases, prompt engineering, and agents
- Experience with MLOps/LLMOps concepts including model evaluation, versioning, monitoring, drift detection, feedback loops, and automated testing
- Strong knowledge of REST APIs, microservices, cloud platforms, CI/CD, Docker, Kubernetes/OpenShift, and observability
- Understanding of AI security, privacy, hallucination mitigation, prompt injection risks, access controls, and responsible AI principles
- Strong problem-solving, stakeholder management, mentoring, and communication skills

Responsibilities

- Lead the design and development of AI/GenAI applications, LLM-powered workflows, agents, copilots, and intelligent automation solutions
- Build production-grade solutions using Python, APIs, LLMs, vector databases, RAG pipelines, embeddings, and orchestration frameworks
- Design and implement Retrieval-Augmented Generation, prompt engineering, function calling, tool use, context management,



and evaluation workflows
- Integrate AI capabilities with enterprise applications, APIs, databases, event-driven platforms, and business workflows
- Define architecture patterns for AI solutions, including model selection, data flow, retrieval strategy, guardrails, observability, and cost optimization
- Remain hands-on with coding, prototyping, debugging, code reviews, performance tuning, and production issue resolution
- Establish engineering standards for AI solution development, including testing, evaluation, monitoring, security, privacy, and responsible AI controls
- Mentor engineers and collaborate with architects, data scientists, ML engineers, product teams, security, DevOps, and business stakeholders
- Use approved AI tools to accelerate coding, testing, documentation, and solution design
- Review and validate AI-generated code and model outputs to ensure correctness, explainability, security, and business alignment

Additional Responsibilities

- Translate business problems into AI use cases with measurable outcomes

Nice To Have

- Experience with Azure AI Foundry, Azure OpenAI, Microsoft.Extensions.AI, Semantic Kernel, or MLflow
- Experience designing enterprise copilots, autonomous agents, AI assistants, or workflow automation platforms
- Knowledge of traditional ML, NLP, deep learning, feature engineering, and model-serving patterns
- Experience with Kafka, event-driven architecture, data lakes,



data warehouses, or real-time analytics platforms
- Familiarity with AI governance, model risk management, auditability, and compliance requirements
- Experience defining AI evaluation metrics such as accuracy, groundedness, relevance, toxicity, latency, cost, and user feedback
- Exposure to frontend or full-stack development for building AI-enabled user experiences

More Skills Python, APIs, model integration, vector databases, embeddings, orchestration frameworks, Milvus, Chroma, FAISS, REST APIs, microservices, cloud platforms, CI/CD, Docker, Kubernetes, OpenShift, observability, AI security, privacy, guardrails, GitHub Copilot, Cursor, Microsoft Copilot, Azure AI Foundry, Microsoft.Extensions.AI, MLflow, Kafka, event-driven architecture, data lakes, data warehouses, real-time analytics, traditional ML, NLP, deep learning, feature engineering, model-serving patterns, full-stack development

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