Lead AI Engineer - Observability

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Job Summary

The Lead Engineer (Generative AI) is a senior technical role responsible for designing, developing, and operationalizing enterprise-scale Generative AI (GenAI) solutions. This position combines deep hands-on expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI architectures with strong technical leadership to deliver secure, scalable, and resilient AI systems.

The role partners across engineering, product, and business teams to translate complex requirements into production-ready AI capabilities aligned with enterprise standards for security, risk, and responsible AI.

Key Responsibilities

1. GenAI Solution Engineering

• Design, develop, and deploy GenAI solutions leveraging:

  • Large Language Models (LLMs)

  • Retrieval-Augmented Generation (RAG) architectures

  • Prompt engineering techniques

  • Agentic AI workflows and orchestration

• Build intelligent systems using frameworks such as Lang Chain, Lang Graph, AWS Bedrock, and Microsoft Foundry Agent Service

• Evaluate emerging tools and frameworks to continuously improve solution quality and innovation

2. GenAIOps & Lifecycle Management

• Lead the end-to-end lifecycle of GenAI solutions, including:

  • Solution architecture and engineering

  • Integration with enterprise systems

  • Secure deployment and release management

  • Monitoring, observability, and continuous optimization

• Implement GenAIOps best practices to ensure scalability, reliability, and cost efficiency

• Establish logging, evaluation, and feedback mechanisms for production AI systems

3. Cloud, Platform & Scalability Engineering

• Architect and deploy GenAI applications across cloud environments (Azure and AWS)

• Design distributed systems capable of supporting high-throughput, low-latency AI workloads

• Leverage modern infrastructure practices:

  • Containerization (Docker)

  • Orchestration (Kubernetes)

  • Infrastructure as Code (Terraform, ARM/Bicep)

• Ensure high availability, performance, and enterprise-grade security

4. Software Engineering & Architecture

• Develop scalable, maintainable applications using Python and microservices-based architectures

• Apply secure coding standards and robust data handling practices for regulated environments

• Build and manage CI/CD pipelines supporting automated testing, deployment, and release management

• Enforce engineering best practices including code reviews, testing, and documentation

5. Technical Leadership & Influence

• Provide architectural leadership and guidance across GenAI initiatives

• Drive critical design decisions for large-scale, complex AI solutions

• Mentor and coach senior engineers and development teams

• Translate business requirements into scalable, secure, and resilient technical solutions

• Partner with stakeholders across product, business, risk, and security functions

Basic Qualifications

• Bachelor’s degree, or equivalent work experience

• Six to eight years of relevant experience

Experience Should Include

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

• 8+ years of experience in software engineering, platform engineering, or AI/ML solutions

• 2+ years hands-on experience with GenAI technologies, including LLMs and RAG architectures and vector databases

• Strong knowledge of agentic AI concepts and frameworks (e.g., Lang Chain, Lang Graph)

• Experience with cloud platforms (Azure and/or AWS)

• Deep understanding of distributed systems and scalable architecture patterns

• Proficiency in Python and microservices-based development

• Experience with Docker, Kubernetes, and Infrastructure as Code tools

• Demonstrated technical leadership and mentoring experience

Preferred Qualifications

• Experience implementing GenAI…