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Location Remote Shift -3PM 12AM Job Description: Key Responsibilities 1.Solution Architecture & Deployment Design and deploy scalable, secure GenAI architectures integrated into customer-facing products. Build REST APIs for AI/ML models and deploy them in containerized environments (Docker, Kubernetes) on cloud platforms (AWS, Azure, GCP). 2.GenAI & LLM Development Fine-tune and optimize generative models including GPT, VAEs, GANs, and transformer-based architectures. Apply techniques like Retrieval-Augmented Generation (RAG) and prompt engineering to enhance model performance and relevance. Work with both commercial and open-source LLMs (e.g., GPT-4, Claude, LLaMA 3.2, Phi). 3.Agentic AI Integration Primary Focus: Build, deploy, and optimize AI agents leveraging frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and Autogen. Implement orchestration strategies, multi-agent collaboration, tool integration, and memory/state management. Drive experimentation to create autonomous or semi-autonomous agents that solve real business workflows and decision-making processes. 4.MLOps & Performance Optimization Establish MLOps pipelines covering model lifecycle: training, CI/CD, monitoring, and retraining. Use tools like Git, Docker, Kubernetes, and vector DBs to ensure efficient and reliable deployment. Optimize resource utilization and infrastructure costs. 5.Cross-Functional Collaboration Partner with engineering, data science, and product teams to align technical solutions with business goals. Effectively communicate complex concepts across diverse technical and non- technical audiences. Stay current with industry advancements and drive innovation in GenAI and AI agent strategy. Skills & Qualifications Required Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain). Hands-on experience in building and deploying AI agents with orchestration, tool use, and state management. In-depth knowledge of LLM architecture, RAGs, embeddings, prompt tuning, and vector databases, agentic AI patterns (ReAct, tool-calling agents, multi-step reasoning, guardrails) Experience with cloud platforms (AWS, Azure, GCP) and containerization. Strong analytical, problem-solving, and communication skills. Data integration experience REST APIs, Google APIs, SQL databases. Comfortable moving data between systems. Experience in Web development: FastAPIs, Typescript, async patterns, building production APIs, React, node.js, Component architecture, hooks, state management, consuming streaming APIs (SSE/WebSocket) Preferred 4+ years of hands-on experience with LLMs and GenAI in production settings. Exposure to agentic AI tools and multi-agent workflows (e.g., CrewAI, LangGraph, Autogen). Familiarity with MLOps and AI deployment best practices. Experience in client-facing or cross-functional AI initiatives. Publications, open-source contributions, or demonstrable projects showcasing AI agent development. .
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.