Senior Generative AI Engineer

WIT Software creates products and solutions for the Telecom industry and has a strong background in Messaging, IP Voice and Video, Unified Communications, Telecom Services, TV Middleware and Mobile Money. With 25 years of experience, WIT works with some of the world's leading Telecom operators across Europe, Japan, the United States and Africa.

WIT is developing an innovative AI-powered platform that leverages the latest advances in Generative AI to enable the creation of next-generation digital services for the Telecom industry. This product has the potential to be adopted by hundreds of mobile operators and service providers, reaching millions of users across multiple geographies worldwide.

We are looking for an experienced Generative AI Engineer to join our Product Team. This is an exciting opportunity for someone passionate about designing scalable software solutions, building production-ready AI applications and applying Generative AI technologies to solve complex real-world challenges.

• Design, develop and maintain scalable backend services for AI-powered applications.
• Design and implement production-ready Generative AI solutions using Large Language Models (LLMs).
• Design, optimize and evaluate Retrieval-Augmented Generation (RAG) or tool-based pipelines and AI orchestration workflows, including chunking strategy, hybrid retrieval and reranking, and retrieval-quality evaluation.
• Design scalable APIs, distributed systems and vector search solutions.
• Evaluate, integrate and optimize foundation models for different use cases.
• Optimize AI systems for performance, scalability and reliability.
• Collaborate with cross-functional teams to define technical solutions and product evolution.
• Contribute to software architecture, technical documentation and engineering best practices.
• Research and evaluate emerging AI technologies.
• Lead design reviews, define engineering standards and mentor engineers on Generative AI best practices.

• Degree in Computer Science, Software Engineering or a related field.
• 5+ years of professional experience in software development.
• 2+ years of hands-on experience designing and building Generative AI applications using Large Language Models (LLMs).
• Hands-on experience with speech and multimodal models, including speech-to-text (STT), text-to-speech (TTS), speech-to-speech and vision-capable LLMs that process images.
• Proficiency using and building applications on top of these models, including real-time (streaming) and Completions/Responses APIs, tool calling, model deployment and model evaluation.
• Strong experience with scalable backend systems and distributed architectures.
• Strong proficiency in Python, including asynchronous and concurrent programming for I/O-bound LLM workloads.
• Solid understanding of software engineering best practices, including design patterns, testing and CI/CD.
• Experience with RESTful APIs, microservices and event-driven architectures.
• Experience building production-ready AI applications.
• Experience defining and applying structured evaluation methodologies for LLM and RAG systems (for example, retrieval and generation quality metrics).
• Knowledge of cloud-native architectures, containerization and orchestration technologies.
• Understanding of responsible AI and security practices, including data privacy, prompt-injection defense and safe handling of model outputs.
• Strong analytical, problem-solving and communication skills.
• Above all, strong product intuition and a sharp sense for user experience, with the ability to build AI solutions that feel intuitive and reliable for end users.

• Hands-on experience designing Retrieval-Augmented Generation (RAG) solutions.
• Proficiency with LangChain, LangGraph or LlamaIndex.
• Experience integrating foundation models such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama or Mistral.
• Knowledge of vector databases, including Pinecone, Milvus, Weaviate or pgvector.
• Experience designing AI Agents and multi-agent systems.
• Understanding of prompt engineering, model evaluation and AI observability.
• Proficiency with Machine Learning frameworks such as PyTorch or TensorFlow.
• Knowledge of cloud platforms (AWS, Azure or Google Cloud).
• Familiarity with Kubernetes, Docker and modern DevOps practices.
• Experience working in Agile software development environments.

Originally posted on Himalayas