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Job Title : Lead AI/ML EngineerJob Purpose :We are seeking a highly experienced Lead AI/ML Engineer to drive the design, development, and deployment of advanced AI solutions across the organization. The ideal candidate will have 610 years of experience in AI/ML, with strong expertise in Generative AI, Large Language Models (LLMs), Computer Vision, and AI platform architecture.As a Lead AI/ML Engineer, you will define AI strategy, architect scalable AI systems, and lead a team of engineers and data scientists to build production-ready AI solutions. You will play a critical role in translating business problems into innovative AI-driven products, leveraging technologies such as LLMs, Retrieval-Augmented Generation (RAG), AI agents, and multimodal AI systems.You will collaborate closely with Product, Engineering, and Data teams to build intelligent, scalable, and high-performance AI platforms that power next-generation applications.Key Responsibilities :AI Strategy & Technical Leadership :- Lead the architecture, design, and implementation of enterprise-scale AI/ML solutions.- Define and drive the AI/ML roadmap, ensuring alignment with business objectives and product strategy.- Provide technical leadership and mentorship to AI/ML engineers and data scientists.- Establish best practices for AI model development, experimentation, deployment, and monitoring.Generative AI & LLM Systems :- Design and develop Generative AI applications using LLMs such as GPT, LLaMA, Gemini, or custom models.- Architect and implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.- Lead initiatives for LLM fine-tuning, prompt engineering, and model optimization.- Design AI agent architectures using frameworks like LangChain, LangGraph, and LlamaIndex.AI/ML Model Development :- Develop and deploy NLP, Computer Vision, and multimodal AI models for real-world business applications.- Implement advanced deep learning architectures using PyTorch, TensorFlow, or Keras.- Identify and evaluate pre-trained and foundation models suitable for specific use cases.- Drive data preprocessing, feature engineering, and dataset curation for model training.AI Platform & Infrastructure :- Design scalable AI infrastructure and MLOps pipelines for model training, deployment, and monitoring.- Deploy AI solutions across cloud platforms (AWS, Azure, GCP) or hybrid/on-premise environments.- Build APIs, microservices, and pipelines to integrate AI capabilities into enterprise applications.- Lead efforts in model optimization, inference acceleration, and resource efficiency.Performance Optimization & Quality :- Conduct model evaluation, benchmarking, and continuous performance optimization.- Optimize AI systems for latency, scalability, and cost efficiency.- Implement testing, monitoring, and observability frameworks for AI systems in production.Collaboration & Innovation :- Work closely with Product, Engineering, and Data teams to define AI-powered product features.- Stay at the forefront of AI research and emerging technologies, evaluating their business impact.- Promote a culture of experimentation, innovation, and knowledge sharing within the AI team.Required Skills & Experience :AI & Machine Learning :- 6 - 10 years of experience in AI/ML development and deployment.- Strong expertise in supervised and unsupervised learning techniques, including regression, classification, clustering, SVMs, and neural networks.Generative AI & LLMs :- Hands-on experience with LLM training, fine-tuning, prompt engineering, and optimization.- Experience building GenAI applications such as chatbots, AI assistants, and document intelligence systems.NLP & Computer Vision :- Strong experience in Natural Language Processing and Computer Vision.- Hands-on expertise with Transformers, OpenCV, YOLO, and R-CNN architecture.AI Agents & Frameworks :- Experience with multi-agent frameworks such as LangChain, LangGraph, and LlamaIndex.Deep Learning Frameworks :- Proficiency in PyTorch, TensorFlow, or Keras.Programming :- Strong programming skills in Python with experience in API development and microservices.Cloud & AI Infrastructure :- Experience deploying AI models on AWS, Azure, or Google Cloud Platform.- Familiarity with MLOps pipelines, model serving, and AI lifecycle management.Vector Databases :- Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate.Performance Optimization :- Experience optimizing LLM inference for speed, cost, and memory efficiency.Leadership & Collaboration :- Proven ability to lead AI projects and mentor engineering teams.- Strong communication skills with the ability to translate business requirements into AI solutions.Good to Have :- Experience with multimodal AI (text, image, video, speech).- Familiarity with Docker, Kubernetes, and containerized AI deployment.- Experience with model serving frameworks such as FastAPI, Flask, or NVIDIA Triton.- Exposure to distributed training and large-scale
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.