Job
Description
As a Generative AI Engineer, you will be responsible for building, deploying, and optimizing multimodal AI services across text, speech, and vision. Your focus will be on working with RAG, synthetic data generation, agent workflows, and integrating STT/TTS/OCR with scalable backend systems. Key Responsibilities: - Designing applications for RAG, CAG, text classification, summarization, image/video generation, OCR, and synthetic data generation in generative pipelines. - Working with STT, TTS, IVR, OCR, and vision inputs to enable seamless AI interactions in multimodal integration. - Developing modular, multi-step orchestrations for document, conversational, and data-based user journeys in AI agent workflows. - Collaborating with DevOps to containerize services, manage Kubernetes orchestration, and implement CI/CD for agile delivery in containerization & deployment. - Instrumenting services using OpenTelemetry, Prometheus, and logging tools to ensure SLO-driven production reliability in observability. - Working cross-functionally with product, data science, and frontend teams to define APIs (REST/GraphQL) and ensure smooth integration in collaboration. - Participating in architecture reviews, writing clear documentation, and mentoring junior engineers and interns in documentation & mentorship. Requirements: - Bachelor's/Masters in Computer Science, Data Science, IT, or related field. - 2-3 years of experience building AI/ML products in Python. - Proficiency in AI-first coding tools like Claude Code, Cursor, Roocode, etc. - Proven experience in deploying GenAI applications and agents in production. - Strong hands-on experience with vector search, embedding-based retrieval, STT, TTS, OCR/vision. - Familiarity with Docker, Kubernetes, frontend development, and CI/CD workflows. - Strong debugging, performance tuning, and cost-optimization skills. - Excellent communication, teamwork, and mentoring abilities. In addition to the above, the technical stack includes mandatory languages & tools like Python (pandas, scikitlearn, PyTorch, Tensorflow, etc.), Git/GitHub, and AWS or GCP. It also includes mandatory generative AI stack components and Agentic AI Frameworks. Nice-to-have skills involve Speech & Vision technologies as well as Observability & Monitoring tools. Cloud & DevOps skills are also considered a plus. If you have domain experience in AgriTech, FinTech, HRTech, or EduTech, experience in reading and implementing research papers, opensource contributions, exposure to managed cloud AI services, or familiarity with React/Next.js integration, those would be considered bonus points. As a Generative AI Engineer, you will be responsible for building, deploying, and optimizing multimodal AI services across text, speech, and vision. Your focus will be on working with RAG, synthetic data generation, agent workflows, and integrating STT/TTS/OCR with scalable backend systems. Key Responsibilities: - Designing applications for RAG, CAG, text classification, summarization, image/video generation, OCR, and synthetic data generation in generative pipelines. - Working with STT, TTS, IVR, OCR, and vision inputs to enable seamless AI interactions in multimodal integration. - Developing modular, multi-step orchestrations for document, conversational, and data-based user journeys in AI agent workflows. - Collaborating with DevOps to containerize services, manage Kubernetes orchestration, and implement CI/CD for agile delivery in containerization & deployment. - Instrumenting services using OpenTelemetry, Prometheus, and logging tools to ensure SLO-driven production reliability in observability. - Working cross-functionally with product, data science, and frontend teams to define APIs (REST/GraphQL) and ensure smooth integration in collaboration. - Participating in architecture reviews, writing clear documentation, and mentoring junior engineers and interns in documentation & mentorship. Requirements: - Bachelor's/Masters in Computer Science, Data Science, IT, or related field. - 2-3 years of experience building AI/ML products in Python. - Proficiency in AI-first coding tools like Claude Code, Cursor, Roocode, etc. - Proven experience in deploying GenAI applications and agents in production. - Strong hands-on experience with vector search, embedding-based retrieval, STT, TTS, OCR/vision. - Familiarity with Docker, Kubernetes, frontend development, and CI/CD workflows. - Strong debugging, performance tuning, and cost-optimization skills. - Excellent communication, teamwork, and mentoring abilities. In addition to the above, the technical stack includes mandatory languages & tools like Python (pandas, scikitlearn, PyTorch, Tensorflow, etc.), Git/GitHub, and AWS or GCP. It also includes mandatory generative AI stack components and Agentic AI Frameworks. Nice-to-have skills involve Speech & Vision technologies as well as Observability & Monitoring tools. Cloud & DevOps skills are also considered a plus. If you have domain e
