AI Engineer – Generative AI & Agentic AI

Role Overview

Qualifications and Experience

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence, or a related discipline.
• Around 10 years of software engineering, architecture, cloud, or platform engineering experience.
• At least 3 years of hands-on AI Engineering experience, including Generative AI and LLM-based application delivery.
• Strong Python proficiency, including NumPy, pandas, and FastAPI, with hands-on PyTorch or TensorFlow experience.
• Hands-on LangChain and LangGraph experience, with mandatory Microsoft Semantic Kernel and Microsoft AutoGen experience.
• Experience implementing RAG with embeddings, vector databases, semantic search, retrieval optimization, and model evaluation.
• Experience deploying models with Amazon Bedrock, Azure OpenAI Service, or Google Vertex AI.
• Experience with microservices, containers, APIs, event-driven architecture, cloud-native services, and evolutionary architecture.
• Experience deploying AI workloads on Kubernetes in cloud-native or hybrid environments.
• Experience with CI/CD, DevOps toolchains, configuration management, deployment pipelines, quality gates, and release governance.
• Relevant cloud, AI engineering, machine learning, or architecture certifications are preferred.

Technical Skills

• Generative AI, Agentic AI, autonomous agents, multi-agent orchestration, and workflow-based AI systems.
• LLMs, embeddings, vector databases, RAG, semantic search, model evaluation, guardrails, observability, and AI governance.
• Semantic Kernel, AutoGen, LangChain, LangGraph, Python, FastAPI, PyTorch or TensorFlow, REST APIs, microservices, and serverless functions.
• Azure, AWS, Kubernetes, containers, CI/CD, DevOps automation, monitoring, and secure software delivery.

Behavioural and Leadership Skills

Responsibilities

• Design, develop, and deploy Generative AI and Agentic AI solutions for enterprise-scale business use cases.
• Build AI-powered applications, autonomous agents, and multi-agent workflows.
• Integrate complex backend services with client-facing web, mobile, and enterprise applications.
• Coordinate with architecture, engineering, product, and business teams to deliver AI solutions.
• Deliver secure, scalable, observable, and governed AI solutions in cloud-native environments.
• Implement LLM applications using embeddings, vector databases, RAG, semantic search, guardrails, and model evaluation.
• Deploy and manage AI workloads across cloud-native and hybrid Kubernetes environments.
• Develop and operate microservices, APIs, containers, serverless functions, and event-driven integrations.
• Establish CI/CD, quality gates, release governance, configuration management, and deployment pipelines.
• Monitor traditional infrastructure, cloud environments, and AI-enabled business applications.
• Analyze technical trade-offs and deliver sustainable, secure, and high-quality solutions.
• Motivate engineers, foster craftsmanship, and influence stakeholders across product, business, architecture, and engineering teams.