Lead AI Engineer

Job Description

Design, build, and scale enterprise-grade AI/GenAI solutions that automate business processes, enhance decision-making, and deliver measurable business outcomes. The role focuses on developing AI-powered applications, intelligent workflows, and integrations by combining enterprise systems, data, APIs, and modern AI technologies in a secure, scalable, and production-ready environment.

Responsibilities

• Design, develop, and deploy AI/GenAI applications, RAG pipelines, and intelligent workflow automation
• Build backend services, APIs, integrations, and orchestration frameworks using LLMs, model APIs, and enterprise data.
• Develop AI-powered knowledge assistants, workflow automation, and decision support solutions.
• Leverage AI-assisted development while ensuring code quality, security, reliability, observability, and governance.
• Define evaluation frameworks to measure AI quality, accuracy, latency, and system performance.
• Collaborate with business, IT, Security, Data, HR, and Finance teams to identify and deliver high-value AI use cases.
• Prototype rapidly and scale solutions into secure, production-ready deployments.
• Optimize AI solutions for performance, scalability, reliability, and cost.
• Develop reusable AI components, frameworks, and best practices to accelerate enterprise adoption.

Required Qualifications

• 7–12+ years of experience in Software Engineering, AI/ML, or GenAI application development.
• Proven experience building and deploying production-grade AI/GenAI solutions.
• Strong expertise in Python, backend development, APIs, system integration, and workflow orchestration.
• Hands-on experience with RAG, LLMs, prompt engineering, tool/agent calling, AI evaluation, and observability.
• Experience integrating enterprise applications, data platforms, and services using APIs and event-driven architectures.
• Strong analytical, systems thinking, and problem-solving skills with the ability to translate ambiguous business problems into scalable AI solutions.

Preferred Qualifications

• Experience with vector databases, embeddings, semantic search, and advanced RAG architectures.
• Knowledge of SQL, data engineering, and data pipelines.
• Experience integrating AI into enterprise applications and business workflows.
• Exposure to intelligent agents, automation platforms, and AI governance frameworks.
• Experience building reusable AI frameworks, accelerators, or enterprise AI platforms.