AI ENGINEER

Position Summary

Design, develop, deploy, and support AI-powered solutions, including Generative AI applications, Intelligent Assistants, Knowledge Retrieval Systems, Intelligent Automation, and AI-enabled business solutions. Work across diverse technologies and platforms to deliver scalable, secure, and business-driven AI capabilities.

Key Responsibilities

• Design, develop, and enhance AI capabilities that address business challenges and deliver measurable business value.

• Build AI/GenAI solutions, intelligent assistants, knowledge retrieval systems, AI agents, and AI-powered automation.

• Implement Retrieval-Augmented Generation (RAG), prompt engineering, and system integrations.

• Evaluate, prototype, and implement appropriate AI models, platforms, and technologies based on business requirements and architectural standards.

• Design scalable, secure, and reusable AI architectures and frameworks to accelerate enterprise AI adoption.

• Manage deployment, monitoring, performance optimization, and AI lifecycle operations.

• Collaborate with business, data, risk, and technology teams to deliver AI solutions.

• Ensure security, governance, privacy, and Responsible AI compliance.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.

• 3-8 years of experience in Software Engineering, AI/ML, Data Engineering, Cloud Technologies, or Digital Solution Development.

• Experience delivering AI-powered applications, automation solutions, or advanced analytics solutions.

• Strong problem-solving, stakeholder management, and communication skills.

Technical Skills

• Programming: Python, SQL, APIs

• AI/ML Development and Generative AI

• Large Language Models (LLMs) and Small Language Models (SLMs)

• Prompt Engineering

• Retrieval-Augmented Generation (RAG)

• Vector Databases and Semantic Search

• Unstructured Data Processing and Data Integration

• AI Agents and Agent-Based Architectures

• MLOps / LLMOps

• CI/CD and Containerization

• Cloud, Hybrid, and On-Premises Architectures

• AI Security, Governance, and Responsible AI

Preferred Qualifications

• Proven experience designing and implementing Retrieval-Augmented Generation (RAG) solutions, including embeddings, vector databases, semantic search, and enterprise knowledge retrieval architectures.

• Experience working with AI platforms, foundation models, orchestration frameworks, and AI development tools.

• Strong understanding of prompt engineering, AI application development, and agent-based architectures.

• Proven ability to design and deliver AI capabilities that address business needs and create measurable business value.

• Experience evaluating and selecting appropriate AI technologies, models, and architectures based on business and technical requirements.