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AI Engineer 3 Yrs (AdTech /Marketing Domain preferred) About the Role We are seeking AI Engineer to design, build, and scale next-generation AI-powered marketing systems. This role combines expertise in Large Language Models (LLMs), agentic AI frameworks, marketing technology ecosystems, and production-grade software engineering. You will be responsible for developing autonomous AI agents and intelligent workflows that enhance campaign execution, audience targeting, content generation, personalization, experimentation, and marketing decision-making. The ideal candidate can bridge cutting-edge AI research with practical business applications and has a track record of shipping reliable AI products in production environments. Key Responsibilities AI Agent Development & Automation - Design, build, and deploy autonomous AI agents that operate in production environments. - Develop agentic workflows leveraging frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar technologies. - Implement multi-agent systems, memory architectures, planning mechanisms, and tool-calling capabilities. - Ensure agent reliability, observability, governance, and operational monitoring. Marketing Intelligence Systems - Build AI-driven solutions for campaign management, audience segmentation, content generation, personalization, and experimentation. - Integrate with marketing platforms, CRM systems, customer data platforms, advertising networks, and analytics tools. - Develop systems that optimize customer acquisition, engagement, conversion, and retention metrics. - Design scalable personalization frameworks powered by AI and behavioural insights. Retrieval-Augmented Generation & Knowledge Systems - Architect and implement enterprise-grade RAG pipelines. - Build knowledge-augmented AI systems that leverage internal and external data sources. - Optimize retrieval quality, context management, grounding, and factual accuracy. - Develop evaluation frameworks for retrieval and generation performance. Required Qualifications AI & Machine Learning - Deep understanding of Large Language Models, Transformer architectures, fine-tuning methodologies, and RLHF concepts. - Solid experience with prompt engineering, tool/function calling, RAG systems, and agent orchestration. - Hands-on expertise with agentic frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent. - Understanding of multi-agent architectures, memory systems, evaluation frameworks, and AI observability. - Experience building and deploying AI agents in production environments. - Proven track record implementing RAG or knowledge-augmented systems beyond proof-of-concept stages. - Experience working with leading model providers including OpenAI, Anthropic, Cohere, and open-source models through Hugging Face. - Ability to evaluate and balance trade-offs between model performance, cost, latency, reliability, and business impact. - Working knowledge of campaign management, audience segmentation, personalization, content generation, and A/B testing workflows. - Familiarity with CRM platforms, marketing automation systems, analytics platforms, and advertising ecosystems. - Understanding of conversion optimization, customer journeys, and growth experimentation. Preferred Qualifications - Experience with multimodal AI systems involving vision, audio, or video. - Background in NLP applications including classification, summarization, entity extraction, and information retrieval. - Knowledge of privacy and compliance frameworks such as GDPR and CCPA in AI-powered systems. - Contributions to open-source AI projects, benchmarks, or research communities. - Experience building AI systems for marketing, customer engagement, or growth-focused use cases.e & responsibilities Preferred candidate profile .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.