Lead AI Engineer

IndiGo; Gurgaon; On-site; INR 9,000,000 - 13,000,000; Full time

Job summary

IndiGo seeks a hands-on Lead Engineer - AI to design, build, and deliver scalable AI-powered applications and platforms.

You will lead architecture, mentor engineers, and accelerate delivery using AI-assisted tools across cloud-native environments.

Qualifications

• 8+ years in software engineering with leadership experience.
• Strong Python and backend/API development experience.
• Hands-on AI/GenAI experience with LLMs, RAG, embeddings, and agents.
• Experience with enterprise AI architectures, data pipelines, and deployment.
• Knowledge of MLOps, monitoring, drift detection, and testing.
• Excellent stakeholder management and communication skills.

Responsibilities

• Lead the design and development of AI/GenAI applications and LLM-powered workflows.
• Mentor engineers and collaborate with architects, data scientists, and product teams.
• Define architecture patterns for AI solutions, data flows, and cost optimization.
• Review AI-generated code and outputs for security, correctness, and business alignment.
• Stay hands-on with coding, debugging, and production issue resolution.

Skills

Python backend/API development

GenAI/LLMs/RAG expertise

Prompt engineering & embeddings

Vector databases

MLOps/LLMOps

Leadership & mentoring

Tools

LangChain

LlamaIndex

Semantic Kernel

Azure OpenAI

OpenAI APIs

Hugging Face

Job description

We are looking for a hands-on Lead Engineer - AI to design, build, and deliver scalable AI-powered applications and platforms. The role requires strong engineering leadership, practical experience with LLMs, RAG, agents, prompt engineering, model integration, MLOps, APIs, and cloud-native deployment, along with the ability to mentor engineers and accelerate delivery using AI-assisted development tools.

Key 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 such as GitHub Copilot, Cursor, Microsoft Copilot, or equivalent 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.

Required Skills and Experience

• 8+ years of software engineering experience, including 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 frameworks and platforms such as LangChain, LlamaIndex, Semantic Kernel, Azure OpenAI, OpenAI APIs, Hugging Face, or similar.
• Strong understanding of AI application architecture, model integration, data pipelines, and production deployment.
• Experience with vector databases such as Azure AI Search, Pinecone, Weaviate, Milvus, Chroma, or FAISS.
• Good understanding of 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.
• Ability to translate business problems into AI use cases with measurable outcomes.
• Strong problem-solving, stakeholder management, mentoring, and communication skills.

Preferred Skills

• 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.

Success Measures

• Delivery of production-ready AI solutions with measurable business impact.
• Improved development speed and quality through responsible use of AI-assisted engineering.
• Reliable AI outputs through strong evaluation, monitoring, and guardrail implementation.
• Reduction in manual effort through automation, copilots, and intelligent workflows.
• Secure and compliant AI solution delivery aligned with enterprise architecture standards.
• Improved team capability through mentoring, reusable patterns, and technical leadership.

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