Senior AI Engineer

• Design and implement advanced Retrieval-Augmented Generation (RAG) systems with sophisticated chunking strategies, multi-vector retrieval, and hybrid search architectures.
• Develop and optimize conversational AI agents with complex multi-turn dialogue management and context awareness.
• Build production-ready voicebot solutions integrating real-time speech processing pipelines.
• Architect and deploy Text-to-Speech (TTS) and Speech-to-Text (STT) systems with low latency and high accuracy.
• Design and deploy PROD-grade AI Agents that can scale up for various use cases.
• Fine-tune large language models using various techniques.
• Optimize model performance through prompt engineering, few-shot learning, and retrieval strategies.
• Implement evaluation frameworks and metrics for LLM outputs and conversational quality.
• Develop custom training pipelines for domain adaptation and specialized use cases.
• Design scalable AI infrastructure supporting real-time inference and high-throughput batch processing.
• Implement efficient vector databases and semantic search systems (Pinecone, Azure Search).
• Build monitoring and observability solutions for AI systems in production.
• Stay current with latest developments in LLM architectures, RAG techniques, and voice AI technologies.
• Experiment with emerging models and frameworks to evaluate applicability to business problems.
• Contribute to technical documentation, best practices, and knowledge sharing within the team.
• Prototype and validate new AI capabilities through POCs and pilots.

• 8+ years of software engineering experience with 4+ years focused on AI/ML engineering.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Deep hands-on experience with advanced RAG systems.
• Proven experience building conversational AI systems.
• Production experience with voicebot development.
• Expertise in TTS and STT technologies.
• Strong experience with LLM fine-tuning.
• Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Experience with MLOps tools and practices (MLflow, Weights & Biases, experiment tracking).
• Strong understanding of API design, microservices architecture, and system integration.

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

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