AI Engineering Lead – Generative AI Solutions

Experience: 15+ Years in Software Engineering
Relevant Experience: 8+ Years in AI/ML, Data Engineering, or Cloud-Native Development
Employment Type: Full-time, Permanent
Work Mode: On-site
Number of Openings: 6

Job Description

We are hiring an experienced AI Engineering Lead to lead the design, development, and production deployment of enterprise-grade Generative AI and Agentic AI solutions.

The ideal candidate will have strong hands-on software engineering expertise, deep knowledge of LLM-based applications, and proven experience in technical leadership, cloud-native engineering, AI security, and production readiness.

This is a full-time, permanent chance based in Abu Dhabi, UAE. The selected candidate will be required to work on-site at the project location and must be willing to relocate if currently based elsewhere.

Key Responsibilities

1. Agentic AI Solution Design

- Design and develop agentic AI solutions using LLMs, RAG, LangChain, LangGraph, MCP, and multi-agent orchestration.
- Build LLM workflows, supervisor/worker architectures, tool calling, and document intelligence solutions.
- Develop reusable AI components and scalable enterprise AI architectures.

2. Hands-on Engineering

- Develop and review production-grade Python applications and AI services.
- Build backend APIs using FastAPI and related frameworks.
- Establish coding standards, testing practices, and code review guidelines.
- Work hands-on with complex technical implementations and troubleshoot engineering challenges.

3. Production Deployment and Cloud Engineering

- Build and deploy containerized AI applications and microservices on AWS.




- Work with Kubernetes, Docker, API gateways, managed identity, and secure networking.
- Support CI/CD automation, deployment pipelines, and production readiness.
- Optimize application performance, scalability, reliability, and operating costs.

4. LLM Evaluation and Quality Assurance

- Develop evaluation frameworks using golden datasets, regression tests, and automated quality checks.
- Measure accuracy, groundedness, latency, and token consumption.
- Implement automated testing, quality gates, and release validation.
- Monitor production performance and continuously improve AI application quality.

5. AI Security and Responsible AI

- Implement guardrails against prompt injection, data leakage, and unsafe tool execution.
- Apply PII protection, secure SDLC practices, and audit logging.
- Establish security testing and responsible AI controls for enterprise deployments.

6. Observability and LLMOps

- Implement OpenTelemetry tracing, structured logging, metrics, dashboards, and alerts.
- Monitor model performance, token usage, latency, and infrastructure costs.
- Maintain operational runbooks and production-support documentation.

7. Technical Leadership

- Lead, mentor, and support AI engineers and software developers.
- Conduct code reviews and architecture reviews.
- Prepare HLDs, LLDs,



sequence diagrams, and Architecture Decision Records (ADRs).
- Collaborate with project managers and cross-functional teams to deliver technical milestones.

Required Skills and Qualifications

- 15+ years of overall software engineering experience.
- 8+ years of experience in AI/ML, data engineering, or cloud-native development.
- 2+ years of hands-on experience shipping LLM/Generative AI solutions to production.
- 3+ years of experience leading engineering teams.
- Strong proficiency in Python, asynchronous programming, FastAPI, Pydantic, and pytest.
- Hands-on experience with LLMs, RAG, embeddings, LangChain, LangGraph, MCP, and agentic workflows.
- Experience with AWS services, including Amazon Bedrock and/or SageMaker AI.
- Knowledge of Docker, Kubernetes, microservices, Kafka, PostgreSQL, Redis, and CI/CD.
- Experience with automated testing, LLM evaluation, production monitoring, and application security.
- Strong problem-solving, technical documentation, communication, and leadership skills.

Preferred Qualifications

- Experience delivering AI solutions in banking or other regulated industries.
- Exposure to document intelligence, credit, lending, or onboarding workflows.
- Familiarity with Terraform, GitHub Actions, Azure DevOps, SonarQube, and security scanning.
- Knowledge of React, TypeScript, or multilingual AI applications.
- Relevant certifications such as Azure AI Engineer, Azure Solutions Architect, CKA, or CKAD.

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline.

Work Location: In person

📌 AI Engineering Lead – Generative AI Solutions (Gurugram)
🏢 Third Party
📍 Gurugram