Agentic AI

Role Description

AI Engineer to design, build, integrate, and operate enterprise-grade AI solutions aligned with current and emerging business priorities . The role is delivery-oriented and requires a strong engineering mindset, practical experience with Generative AI technologies, and the ability to translate business requirements into scalable, production-ready applications.

The successful candidate will contribute across the full solution lifecycle, including requirements analysis, technical design, prototyping, backend and API development, data ingestion, LLM integration, evaluation, deployment, documentation, and operational support. The role requires close collaboration with product owners, business stakeholders, architects, platform teams, security specialists, and engineering teams across multiple locations.

Required Skills and Experience

• Bachelor’s degree (or equivalent) in Computer Science, Engineering, Data Science, Mathematics, Statistics, Physics, or a related quantitative discipline.
• 4+ years of hands-on experience in software engineering, AI engineering, data engineering, or related technical roles, with demonstrable ownership of delivering production-grade applications.
• Strong Python development skills, including experience with modern backend frameworks such as FastAPI or Flask, API design, unit testing, debugging, and software engineering best practices.
• Proven experience designing, building, and deploying Generative AI / LLM-powered applications in enterprise environments.
• Strong understanding of Large Language Models (LLMs), prompt engineering techniques, grounding concepts, model limitations, and approaches for evaluating AI-generated outputs.
• Practical experience implementing scalable and maintainable software solutions across the full Software Development Lifecycle (SDLC), including requirements analysis, development, testing, deployment, and operational support.
• Hands-on experience with SQL and relational databases, including data modelling, data integration, and performance optimization.
• Experience building and deploying cloud-native applications using container technologies such as Docker and cloud platforms such as Google Cloud Platform (GCP), AWS, or Azure.
• Understanding of cloud architecture principles, microservices, API integration patterns, authentication, authorization, monitoring, and operational support.
• Experience with CI/CD pipelines, source control systems (Git), code reviews, and development lifecycle management.
• Strong analytical, problem-solving, and communication skills, with the ability to work directly with business stakeholders and translate requirements into technical solutions.
• Demonstrated ability to quickly learn and adopt new technologies, frameworks, and tools as business and technology needs evolve.