About the company:

EMB Global is a technology services and AI transformation partner headquartered in Gurugram, India, operating across 20+ countries. Founded in 2018, the company has grown into an AI delivery engine for enterprises, helping organisations move beyond pilots and demos into AI that is embedded directly into decision systems, workflows, and measurable business outcomes. As an AWS Advanced Tier Services Partner, we build enterprise-grade cloud and AI platforms for leading enterprise and government clients, backed by a proprietary product suite spanning intelligence, execution, and monitoring layers for agentic AI systems.

About the Role:

We are looking for a Lead AI Engineer to own the technical direction of our production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on leadership role: you will architect and build production systems, set engineering standards and best practices, mentor a team of AI engineers, and work directly with architects, product teams, and clients to take solutions from prototype to scale.

Key Responsibilities:

- Lead the design, architecture, and delivery of Agentic AI solutions, including agent workflows, tool calling, memory, and human-in-the-loop systems.

- Own the technical roadmap for production-grade GenAI applications built on LLMs, RAG pipelines, and prompt engineering techniques.

- Set standards for AI/ML pipelines, model evaluation frameworks, and monitoring systems, and ensure the team follows them consistently.

- Guide integration of AI solutions with enterprise applications through APIs and cloud-native architectures.

- Define and enforce AI safety, guardrails, observability, and performance optimization practices across projects.

- Mentor and technically guide a team of AI engineers, conducting code and design reviews.

- Act as the primary technical point of contact for clients and cross-functional teams, translating business requirements into scalable AI architectures.

- Collaborate with leadership on hiring, project scoping, and technical decision-making for the AI engineering practice.

Required Qualifications:

- 8-13 years of software engineering experience, including prior experience leading engineers or technical workstreams.

- Proven track record of architecting and deploying AI/ML solutions in production environments at scale.

- Deep expertise in LLMs, RAG, Agentic AI frameworks (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, etc.), and prompt engineering.

- Strong Python skills; experience with Node.js/TypeScript is a plus.

- Extensive experience with AWS and/or Azure cloud-native architectures, APIs, containers, and vector databases.

- Strong grasp of AI safety, observability, model evaluation, and scalability best practices, with the ability to set standards for others.

- Demonstrated ability to mentor engineers and lead technical decision-making in a fast-paced environment.

Preferred Qualifications:

- Experience with model fine-tuning, open-source models, or self-hosted AI deployments.

- Experience integrating enterprise platforms such as Salesforce, SAP, ServiceNow, or Microsoft Dynamics.

- AWS/Azure certifications and contributions to the AI community are a plus.

- Experience presenting technical solutions directly to enterprise clients or senior stakeholders.