Experience:

• 5+ Years
• Minimum 5 years of hands-on experience in Artificial Intelligence, Machine Learning, and Deep Learning
• 2+ years of experience building production-ready Generative AI and LLM-based applications

Responsibilities:

• Design, develop, and deploy production-ready AI/ML solutions for enterprise applications
• Build and optimize Large Language Model applications, AI agents, and Retrieval-Augmented Generation pipelines
• Fine-tune, evaluate, and optimize open-source and commercial AI models for specific business use cases
• Develop scalable APIs and AI services using Python frameworks such as FastAPI or Flask
• Build intelligent automation workflows using AI, vector databases, and orchestration frameworks
• Design and implement machine learning pipelines for data preprocessing, feature engineering, model training, validation, and deployment
• Collaborate with Product Managers, Architects, UI/UX Designers, and DevOps teams to deliver AI-powered products
• Optimize model inference performance, latency, and infrastructure costs
• Mentor junior AI engineers and participate in architecture discussions, technical reviews, and code reviews
• Research and evaluate emerging AI technologies and recommend adoption where appropriate
• Ensure AI applications follow security, privacy, and responsible AI best practices

Additional responsibilities:

• Strong knowledge of software architecture and design patterns
• Experience building scalable AI SaaS platforms
• Understanding of API security, authentication, and data privacy
• Excellent debugging and performance optimization skills
• Familiarity with Agile/Scrum development methodology

Nice to have:

• Experience with Haystack AI framework
• Kubernetes experience
• Computer Vision skills including OpenCV, YOLO, OCR, image classification, object detection, pose estimation
• Voice AI skills including Speech-to-Text, Text-to-Speech, real-time voice assistants, voice activity detection, audio streaming

More skills:

Python, SQL, JavaScript, TypeScript, Machine Learning, Deep Learning, NLP, Large Language Models (GPT, Claude, Llama, Qwen, Mistral, Gemma), Multi-agent workflows, Structured outputs, Function calling, Tool integration, Retrieval-Augmented Generation (RAG), Semantic search, Haystack, PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, Pinecone, Qdrant, Milvus, ChromaDB, FAISS, FastAPI, Flask, RESTful APIs, WebSockets, Celery, RQ, PostgreSQL, MongoDB, Redis, AWS, Azure, Google Cloud Platform, Docker, Kubernetes, Git, CI/CD pipelines, Linux, MLflow, Weights & Biases, Model versioning, Model deployment, Monitoring and observability, GPU optimization, Inference serving, OpenCV, YOLO, OCR, Image classification, Object detection, Pose estimation, Speech-to-Text, Text-to-Speech, Real-time voice assistants, Voice activity detection, Audio streaming