Techuz - Software, AI and Automation

We are seeking a highly skilled AI Engineer with strong expertise in building, deploying, and optimizing AI/ML solutions for production-grade applications. The ideal candidate should be proficient in LLMs, prompt engineering, AI automation, vector databases, computer vision, cloud services (AWS/Azure/GCP), and integrating AI into web/mobile applications. You will work closely with our engineering, product, and design teams to develop intelligent features, automate workflows, improve system efficiency, and deliver AI-driven solutions to our global clients. Key Responsibilities 1. AI/ML Development Design, build, and deploy AI models (LLMs, NLP, CV, predictive analytics). Fine-tune and optimize models using Hugging Face, OpenAI, Llama, etc. Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases (Pinecone, Chroma, Weaviate, Elastic). Build NLP-based features such as chatbots, summarizers, sentiment analysis, and automated document understanding. Work on computer vision tasks: OCR, object detection, blueprint analysis, anomaly detection, image classification. (Optional) 2. Backend & Integrations Develop and integrate AI services with backend APIs (Node.js, Python – FastAPI/Django/Flask). Build scalable ML pipelines and microservices for inference. Integrate with third-party APIs (OpenAI, AWS AI, Azure Cognitive Services, Google Vision/VertexAI). Implement model monitoring, logging, and analytics for performance and accuracy tracking. 3. Prompt Engineering & Automation Design high-quality prompts for various use cases: legal, medical, e-commerce, LMS, chat automation, support agents, etc. Build agentic workflows using LangChain, LlamaIndex, or custom pipelines. Create automated workflows to classify data, parse documents, extract insights, or perform domain-specific actions. 4. Data Engineering Prepare, clean, and analyze datasets for training and inference. Work with structured and unstructured data (text, audio, image, video). Create embeddings, manage vector databases, and handle large datasets efficiently. 5. Cloud & DevOps Deploy AI models on AWS, Azure, GCP. Optimize inference cost and performance. Use Docker, Kubernetes for scalable deployments. (Optional) Implement CI/CD pipelines for AI services. 6. Collaboration & Documentation Work with PMs, designers, and clients to convert business requirements into AI solutions. Prepare clear documentation on system design, workflows, and AI pipelines. Support code reviews and mentor junior AI engineers. Required Skills & Experience Technical Skills Strong Python skills (PyTorch / TensorFlow / Transformers). Experience with LLMs: OpenAI GPT series, Llama, Mistral, Hugging Face models. Strong understanding of NLP, CV, and ML fundamentals. Experience with vector DBs (Pinecone, Chroma, Weaviate, Elasticsearch). Experience building RAG systems and agentic workflows. Experience with AWS AI (Comprehend, Bedrock), Google Vision, Azure OpenAI. Experience integrating AI with Node.js/React/Flutter applications. Experience with microservices, REST APIs, webhooks, and event-driven systems. Experience with databases: MySQL, PostgreSQL, MongoDB. Soft Skills Strong analytical and problem-solving mindset. Excellent communication and client-coordination skills. Ability to work in agile/sprint-based environments. Self-driven and proactive in researching new AI techniques. Nice to Have Knowledge of speech-to-text/voice agents (Whisper, Twilio, Dialogflow). Experience with agent frameworks (AutoGen, CrewAI, LangGraph). Experience in healthcare, legal, finance, LMS, or e-commerce domains. Experience with MLOps: MLFlow, Weights & Biases. Ability to deliver end-to-end AI features independently. Experience Required 3–7 years of experience in AI/ML development (or strong hands-on experience with LLMs and production AI systems).