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.
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