AI/ML Engineer

Key Responsibilities

• Design, develop, and deploy advanced conversational AI systems, including customer onboarding agents and support chatbots for platforms like WhatsApp.
• Process, transcribe, and diarize audio conversations to create high-quality datasets for fine-tuning large language models (LLMs).
• Develop and maintain robust, scalable infrastructure for AI model serving, utilizing technologies like FastAPI, Docker, and cloud platforms (e.g., Google Cloud Platform).
• Integrate and leverage knowledge graphs and contextual information systems to create more personalized, empathetic, and goal-oriented dialogues.
• Engineer and implement retrieval-augmented generation (RAG) systems to enable natural language querying of internal company documents, optimizing for efficiency and informativeness.
• Fine-tune and deploy generative models like Stable Diffusion for custom asset creation, with a focus on improving precision and reducing generative artifacts (FID score).
• Collaborate with cross-functional teams, including product managers and designers, to build user-friendly interfaces and tools that enhance productivity and user experience.
• Contribute to the research and publication of novel AI systems and models.
  

Qualifications And Skills

• Education: Bachelor of Engineering (B.E.) in Computer Science or a related field.
• Experience: 3+ years of professional experience as a Machine Learning Engineer or in a similar role.
• Programming Languages: Expertise in Python and a strong command of SQL, and JavaScript.
• Machine Learning Libraries: Hands-on experience with PyTorch, Scikit-learn, Hugging Face Transformers, Diffusers, Librosa, LangGraph, and the OpenAI API.
• Software and Tools: Proficiency with Docker and various databases including PostgreSQL, MongoDB, Redis, and Elasticsearch.
• Core Competencies:
• Experience developing end-to-end AI pipelines, from data processing and model training to API deployment and integration.
• Familiarity with MLOps principles and tools for building and maintaining production-level machine learning systems.
• A portfolio of projects, publications, or open-source contributions in the fields of NLP, Computer Vision, or Speech Analysis is a plus.
• Excellent problem-solving skills and the ability to think strategically to deliver optimized and efficient solutions.