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Experience : 56 Years Job Type - Full-Time Overview We are looking for a highly skilled and communication-driven Python AI/ML Engineer with 56 years of hands-on experience in designing, developing, and deploying scalable AI/ML solutions. The ideal candidate should have strong expertise in Python, modern AI frameworks, LLMs, data pipelines, and cloud-based deployments, along with excellent communication and client interaction skills. The candidate will work closely with cross-functional teams, clients, and stakeholders to build intelligent systems, optimize AI workflows, and contribute to architecture and technical decision-making. Key Responsibilities Design, develop, and maintain scalable AI/ML systems using Python Build and deploy Machine Learning and Deep Learning models for real-world applications Work on LLM-based systems, RAG pipelines, AI agents, and conversational AI solutions Develop APIs and backend services for AI-powered applications Handle data preprocessing, feature engineering, training, evaluation, and optimization Integrate AI/ML models with web/mobile/backend systems Work with vector databases, embeddings, and semantic search systems Optimize inference performance, latency, and cost efficiency Participate in technical architecture discussions and solution planning Collaborate with product managers, designers, frontend/backend teams, and clients Review code, mentor junior developers, and maintain development standards Prepare technical documentation, reports, and client-facing explanations Stay updated with the latest advancements in AI, LLMs, and automation technologies Required Skills Core Technical Skills Strong expertise in Python Excellent understanding of AI/ML fundamentals Hands-on experience with: PyTorch / TensorFlow Scikit-learn Pandas / NumPy FastAPI / Flask / Django Experience with: LLMs (OpenAI, Claude, Gemini, Llama, etc.) LangChain / LangGraph / CrewAI / AutoGen RAG architecture Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Prompt Engineering AI Agents & Automation workflows Knowledge of: - NLP - Computer Vision - Embeddings & Semantic Search - Fine-tuning and model optimization - Experience with REST APIs and microservices architecture - Strong understanding of SQL/NoSQL databases - Familiarity with Docker, CI/CD, and cloud platforms (AWS/GCP/Azure) - Experience in model deployment and production monitoring Communication & Soft Skills - Excellent English communication skills (written and verbal) - Strong client handling and presentation abilities - Ability to explain technical concepts to non-technical stakeholders - Strong analytical and problem-solving mindset - Good leadership and mentoring capabilities - Ability to work independently and collaboratively in a fast-paced environment Preferred Qualifications - Experience working on production-scale AI systems - Experience with synthetic data generation and AI evaluation systems - Knowledge of MLOps workflows - Exposure to real-time systems, WebSockets, or streaming pipelines - Experience integrating OpenAI or other AI APIs into production applications - Prior experience leading AI/ML teams or projects is a plus Nice to Have - Experience with: - YOLO / Object Detection - OCR systems - Speech-to-Text / Text-to-Speech - Multi-modal AI systems - Knowledge Graphs - Autonomous AI agents - Open-source contributions or research publications Compensation: Up to 100,000.00 per month Benefits: Flexible scheduleLeave encashmentWork Location: In person .
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Most developer ads you'll see are written in English: 18,397 of the 24,795. That doesn't tell you whether the job itself requires you to speak English or work in English daily, so read each posting carefully. Don't assume the language of the ad matches the language of the team.
The employers posting the most developer roles are Link Group (369 jobs), Upvanta (257), jobgether (226), and OfferZen (212). If you're applying to any of these, research their hiring patterns. They move fast and post often, which means they're either scaling hard or replacing people who didn't fit.
In your interview, expect this: 'Walk me through the last time you had to debug something that took you more than an hour. What was it, what did you try first, and what would you do differently?' Hiring managers ask this to see if you think systematically or just try random fixes. Have a real example ready with specifics.