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Key Duties & Responsibilities AI & Machine Learning Development: Design, build, and deploy AI/ML applications and generative AI solutions using Python, modern web programming frameworks, and structured relational databases (SQL/MySQL/PostgreSQL). System Integration & Modernization: Facilitate seamless integration of AI applications and agents with existing e-learning platforms and architecture; troubleshoot and resolve technical application issues effectively. Data Engineering & Workflow Orchestration: Build efficient data pipelines, develop data-driven web dashboards/chat interfaces, and leverage orchestration tools, vector databases, and LLM APIs. Risk & Security Management: Enforce strict data confidentiality protocols when handling restricted process data. Proactively identify potential issues—including data security, ethical/legal compliance, system reliability, vulnerabilities, and performance bottlenecks—and consult with senior team members in a timely manner. Deployment & Best Practices: Containerize and deploy software applications using Docker and Git version control; support systems in cloud environments. Continuous Improvement: Continuously monitor emerging AI technologies, methodologies, and compliance frameworks to expand application capabilities and keep systems up-to-date. Requirements Higher Diploma / Degree in Information Technology or related subjects At least 3 years of post-qualification Information Technology experience Proficiency in developing AI/ML applications in Python Strong knowledge of web programming, structured data query tools, and relational databases (SQL / MySQL / PostgreSQL) Solid foundation in system integration, data engineering, knowledge management, collaborative tools, and standard development environments Hands-on exposure to orchestration tools (e.g., Dify, n8n, LangChain, LlamaIndex ), Retrieval-Augmented Generation (RAG), vector databases, LLM APIs, and AI Agents Experience building data-driven dashboards or chat interfaces using tools like Streamlit or Dash Practical experience deploying applications using Docker and version control via Git Familiarity with NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch, or Keras Knowledge or experience utilizing cloud services Strong problem-solving ability with a proactive approach to risk identification and timely communication with seniors. High adherence to data privacy, confidentiality, and security standards. Commitment to continuous learning regarding AI limitations, ethical frameworks, and technology trends. If interested for the above position, please send detailed resume with current and expected salary as well as date of availability by sending email to applyjob@clts.com For more job opportunity, please visit our website: www.clts.com The personal information is collected strictly for recruitment purpose only.
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.