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We Have 2 different requirement below is the JD's for both the requirement: ROLE-1 : AI Infrastructure Engineer (GPU Computation) ROLE 2:AI Software Engineer (Generative AI) 1. AI Infrastructure Engineer (GPU Computation) Experience Required: 5+ years in AI/ML infrastructure or high-performance computing Location: Delhi NCR Infrastructure Environment Hybrid both cloud (e.g. AWS) and on-premises GPU clusters Key Responsibilities - Design, configure, and optimize GPU compute pipelines on-premises and cloud-based to hit target utilization levels (~90%). - Profile training and inference workloads to identify and remove hardware-level bottlenecks, regardless of where they're hosted. - Recommend and implement scaling, batching, and parallelization strategies across on-prem and cloud GPU clusters. - Decide, workload by workload, whether on-premises or cloud compute is the better fit, and manage the two consistently. - Collaborate continuously with the AI Software Engineer to optimize model performance, compute utilization, deployment architecture, and experimentation speed. - Set up monitoring and reporting on GPU utilization, training throughput, and compute efficiency metrics across both environments. - Identify opportunities to improve platform infrastructure capacity planning, cost efficiency, reliability ahead of being asked. - Support a hypothesis-driven experimentation culture by enabling rapid, low-friction infrastructure for rapid prototyping and iteration. Success Metrics - GPU utilization against target (~90%). - Training/inference throughput and experiment turnaround time. - Infrastructure reliability (uptime, incident frequency). - Cost optimization across cloud and on-premises spend. Requirements - 5+ years of hands-on experience in AI/ML infrastructure or high-performance computing. - Strong expertise in GPU architecture and tooling (e.g., NVIDIA CUDA, TensorRT). - Experience with distributed training/inference frameworks (e.g., DeepSpeed, Horovod, Ray). ROLE-2 2. AI Software Engineer (Generative AI) Experience Required: 5+ years building and deploying generative AI / LLM-based solutions Location: Delhi NCR Key Responsibilities - Design, build, and fine-tune generative AI models and pipelines for business use cases, covering the full lifecycle prompt engineering, RAG implementation, agentic workflows, model evaluation, guardrails, and production monitoring. - Collaborate continuously with the AI Infrastructure Engineer to optimize model performance, compute utilization, deployment architecture, and experimentation speed. - Foster a hypothesis-driven experimentation culture by rapidly prototyping, measuring outcomes, and iterating based on feedback. - Evaluate and benchmark internal AI solutions (e.g., EAP) against commercial offerings such as Microsoft Copilot and Excel Insights, to identify gaps and recommend improvements. - Identify opportunities to improve AI platform capabilities, usability, and adoption through continuous experimentation and innovation. - Incorporate feedback from retrospectives to continuously enhance model and platform performance and close the loop visibly with the client. - Apply LLMOps/MLOps practices for model deployment, versioning, and monitoring in production. Success Metrics - Experiment turnaround time. - AI feature/tool adoption rate across business functions. - Model accuracy and reliability against agreed benchmarks. - User satisfaction with delivered AI capabilities. - Number of successful production deployments. Requirements - 5+ years of hands-on experience building and deploying generative AI / LLM-based solutions. - Strong experience with generative AI frameworks and tooling (e.g., LangChain, LangGraph, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs). - Experience with RAG pipelines, vector databases (e.g., Pinecone, Milvus, FAISS), and Model Context Protocol (MCP) or similar agentic/tool-use integration patterns. - Familiarity with AI evaluation frameworks and building in guardrails for production AI systems. - Working knowledge of FastAPI, Docker/Kubernetes, and CI/CD practices for AI workloads. - Proven experience productionizing AI/ML models for real-world business use cases. - Ability to work in a fast-paced, experiment-driven environment with tight feedback loops. .
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.