Lead ai engineer

Job Description

Design, build, and scale enterprise-grade AI/Gen AI 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/Gen AI 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 Gen AI application development. Proven experience building and deploying production-grade AI/Gen AI 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.