AI Engineering Lead / Manager | NDA

Accountabilities

• Provide technical leadership and advisory support to engineering and consulting teams on AI-assisted software engineering, developer productivity, system architecture, and modern engineering practices.


• Guide the adoption of AI tools such as Claude Code, Cursor, Codex, and GitHub Copilot to improve software development workflows and efficiency.


• Define and refine architectural approaches for systems, including microservices, APIs, data flows, integrations, and CI/CD pipelines.


• Translate business requirements into clear technical designs and implementation strategies aligned with delivery goals.


• Spend a portion of time hands-on building and supporting AI-powered applications, including LLM-based systems, RAG pipelines, and AI agents.


• Design, implement, and optimize retrieval-augmented generation (RAG) systems with a focus on performance, cost efficiency, and accuracy.


• Contribute to engineering execution through code reviews, testing strategies, documentation, and implementation support.


• Advise on engineering best practices including automated testing, secure development, clean code principles, and scalable delivery workflows.


• Collaborate with cross-functional stakeholders including product, design, architecture, and platform teams in a client-facing environment.


• Support continuous improvement of engineering maturity across people, processes, and technology.

Requirements

• Strong background in software engineering, backend development, full-stack engineering, or software architecture.


• Extensive hands-on experience with Python in production environments.


• Experience designing and developing microservice-based systems using REST, GraphQL, or gRPC APIs.


• Familiarity with API frameworks such as FastAPI, OpenAPI, Swagger, or similar technologies.


• Practical experience with AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Codex, or equivalents.


• Hands-on experience with LLM-based applications, including prompt engineering, structured prompting, RAG systems, and AI agents.


• Deep understanding of transformer-based models and large language model architectures.


• Experience building and optimizing retrieval-augmented generation pipelines, including handling hallucination risks and performance trade-offs.


• Strong understanding of software engineering fundamentals, including data structures, algorithms, testing strategies, and OOP principles.


• Knowledge of tokenization, context limitations, model behavior, and cost-performance optimization in LLM systems.


• Ability to translate complex business needs into technical solutions and implementation roadmaps.


• Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.


• Experience working in client-facing or consulting environments is highly desirable.


• Comfortable working with partial overlap with US time zones.


• Experience with cloud platforms (AWS, GCP, or Azure), databases (SQL or NoSQL), or enterprise environments is a plus.

Benefits

• Opportunity to work on cutting-edge AI engineering transformation projects for enterprise clients.


• High-impact consulting engagement at the intersection of AI, software engineering, and developer productivity.


• Exposure to advanced LLM applications, RAG architectures, and AI agent systems in real-world environments.


• Client-facing role with significant ownership and visibility across technical and business stakeholders.


• Flexible consulting engagement structure with international collaboration.


• Opportunity to shape engineering practices and AI adoption strategies at scale.


• Hands-on experience with modern AI tooling and enterprise-grade system design.


• Exposure to global consulting environments and large-scale transformation programs.


• Competitive compensation aligned with senior-level consulting expertise and AI engineering experience.

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