Lead/Staff Engineer - Applied AI

Accountabilities:

• Architect, build, and deploy autonomous AI agents capable of executing complex workflows across sales, messaging, scheduling, and business operations.
• Develop and fine-tune LLM solutions using both open-source models and API-based platforms, tailoring them to specific data, product, and customer requirements.
• Design and scale robust Retrieval-Augmented Generation (RAG) systems and vector search infrastructure to support accurate, context-rich, real-time generation.
• Develop and continuously refine prompt engineering, context construction, function calling, and agent tool-use strategies using LangChain or comparable orchestration frameworks.
• Apply advanced data science techniques, including predictive modeling, A/B testing, scoring, clustering, segmentation, causal inference, and time-series forecasting, to improve AI capabilities and broader product experiences.
• Partner with backend, infrastructure, and product teams to develop reusable GenAI infrastructure covering model serving, prompt versioning, logging, evaluation systems, monitoring, and feedback loops.
• Establish rigorous evaluation and experimentation processes to monitor agent performance, hallucination rates, reliability, and real-world effectiveness.
• Lead initiatives from prototype through production, ensuring strong engineering practices, observability, scalability, and ongoing model improvement.
• Influence the AI roadmap and technical direction while mentoring engineers and contributing to engineering standards, architecture decisions, and best practices.
• Translate technical possibilities and constraints into practical product solutions while maintaining a strong focus on customer and business outcomes.

Requirements:

• 8+ years of experience in Data Science, Machine Learning, Applied AI, or a closely related field, with a demonstrated history of delivering production-grade AI and ML systems.
• Hands-on expertise with LLM technologies, including fine-tuning, prompt engineering, embeddings, function-calling agents, and model evaluation.
• Strong experience designing and implementing RAG systems using vector databases such as FAISS, Pinecone, or Weaviate.
• Experience working in cloud-native environments, particularly GCP and AWS, with experience deploying models using technologies such as PyTorch, Hugging Face Transformers, and MLOps tooling.
• Experience with LangChain or similar agent orchestration frameworks, including the ability to design multi-step, tool-augmented agents.
• Strong Python programming skills combined with sound engineering practices around CI/CD, testing, version control, and production software development; familiarity with TypeScript is also valuable.
• Strong foundation in core data science concepts, including supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting.
• Proven ability to take AI and ML solutions from experimentation and prototyping through production deployment, monitoring, observability, and iterative improvement.
• Strong understanding of experimentation and evaluation methodologies for AI systems, including performance measurement, feedback loops, and real-world effectiveness.
• Ability to work independently while collaborating effectively across product, engineering, infrastructure, and other cross-functional teams.
• Demonstrated leadership capabilities, including driving initiatives, influencing technical direction, mentoring peers, and operating effectively in a fast-paced environment.
• Strong product sense and communication skills, with the ability to connect complex technical considerations to product objectives and customer needs.

Benefits:

• Remote role with the flexibility to work from anywhere in India.
• High-autonomy position with substantial ownership over applied AI and Generative AI initiatives.
• Opportunity to build AI agents and intelligent systems operating at significant scale and supporting millions of users.
• Exposure to advanced technologies spanning LLMs, RAG, vector search, agent orchestration, MLOps, and large-scale data science.
• Opportunity to influence AI product strategy, technical standards, and the development of reusable GenAI infrastructure.
• Collaboration with backend, infrastructure, product, and engineering specialists in a globally distributed, remote-first environment.
• Strong opportunities for technical leadership, mentoring, and professional growth through high-impact AI initiatives.
• Fast-paced engineering culture that values initiative, ownership, innovation, and practical execution.