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Key Responsibilities Agentic System Development: Design and implement intelligent agent architectures capable of autonomous reasoning, task planning, and execution. Computer Vision Integration: Leverage cutting-edge models (e.g., YOLO, Vision Transformers) to enable AI agents to interpret, analyze, and process visual data effectively. Knowledge Retrieval Optimization: Architect and maintain advanced Retrieval-Augmented Generation (RAG) pipelines, ensuring seamless integration of multi-modal data for context-aware responses. Workflow Orchestration: Utilize industry-standard frameworks, including LangChain and LangGraph, to build modular, resilient, and scalable AI service chains. Cloud Deployment & Scalability: Manage the end-to-end lifecycle of AI solutions, including deployment, monitoring, and performance optimization on cloud infrastructure (AWS, Azure, or GCP). Cross-Functional Collaboration: Collaborate with product and engineering teams to translate abstract business objectives into actionable technical prototypes and production-ready systems. Technical Requirements Experience: 23 years of professional experience in software development with a dedicated focus on AI/ML lifecycle management. Programming: Mastery of Python for robust, production-quality code. AI/ML Foundational Knowledge: Proven expertise in LLMs, RAG, and Computer Vision architectures. Frameworks & Tooling: Orchestration: Hands-on experience with LangChain or LangGraph. Vision Libraries: Proficiency in OpenCV, PyTorch, YOLO, Detectron2, SAM, or ViT. Database Management: Experience with vector databases such as Chroma, Pinecone, or FAISS. Cloud Infrastructure: Demonstrated experience with major cloud platforms (AWS, Azure, or GCP). Problem-Solving: Proven ability to decompose complex technical requirements into scalable, iterative development milestones. Preferred Qualifications Candidates possessing the following attributes will be given priority: Production Lifecycle: Experience transitioning models from development environments (Jupyter/Colab) to live, high-concurrency APIs. Edge AI & Inference: Proficiency in model optimization techniques such as ONNX or TensorRT for low-latency inference. MLOps Proficiency: Experience with containerization (Docker/Kubernetes) and tracking tools (MLflow, Weights & Biases). Advanced Training: Proven experience with Parameter-Efficient Fine-Tuning (PEFT/LoRA) and custom training on specialized datasets. Multi-Modal Expertise: Practical application of modern multi-modal architectures (e.g., Llama Vision, Claude 3.5 Sonnet, GPT-4o). Community Engagement: Evidence of active contributions to open-source AI projects or a strong portfolio of independent AI/ML developments on GitHub. Benefits: Health insurance Leave encashment Provident Fund .
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.