Senior AI Engineer

Senior AI Engineer

This role focuses on designing and deploying production-grade AI systems that connect LLMs, vector databases, and backend services across multiple software companies within a larger portfolio ecosystem. You will work at the intersection of AI engineering and system integration, building scalable architectures that enable reusable, standardized AI capabilities. The environment is highly technical and impact-driven, where your work directly shapes how multiple businesses operationalize AI at scale. You will move beyond experimentation into production systems that must be reliable, observable, and cost-efficient. The role involves collaborating across engineering teams, defining integration patterns, and ensuring AI systems are safely and efficiently embedded into existing infrastructures. It is ideal for engineers who enjoy solving complex backend integration challenges and creating reusable engineering frameworks with long-term impact.

Accountabilities:

• Design and implement end-to-end AI integration architectures connecting LLM APIs, vector databases, and backend systems across multiple production environments.
• Build reusable ML and AI infrastructure components such as data pipelines, model serving layers, and evaluation frameworks that can be standardized across portfolio companies.
• Define and enforce best practices for AI system integration, including governance, reproducibility, monitoring, and deployment standards.
• Lead system design reviews for AI initiatives, identifying performance bottlenecks and recommending scalable architectural improvements.
• Optimize production AI systems for latency, cost, and efficiency through profiling, tuning, and infrastructure improvements.
• Mentor engineering teams across different companies on production AI patterns, safe deployment practices, and scalable system design.
• Collaborate cross-functionally to ensure AI systems integrate seamlessly into existing backend ecosystems without requiring major architectural overhauls.

Requirements:

• 5+ years of experience in backend engineering or system integration roles, with strong exposure to production-grade distributed systems.
• Proven experience building and scaling integrations across multiple services, APIs, or platforms in complex environments.
• Strong proficiency in Python and SQL for building backend services, data pipelines, and AI system integrations.
• Hands-on experience deploying production AI systems such as LLM applications, vector search, or ML inference pipelines.
• Deep understanding of backend architecture and system design, with the ability to integrate AI tools into existing systems efficiently.
• Experience building observable, versioned, and reproducible systems rather than experimental or prototype-level solutions.
• Strong communication and mentoring skills, with the ability to guide engineers and influence architectural decisions across teams.
• Nice to have: experience with MLOps tools (MLflow, W&B, SageMaker), Kubernetes, cloud platforms (AWS/GCP/Azure), or RAG systems.

Benefits:

• Remote-first work setup based in India with flexible location options.
• Opportunity to work across multiple portfolio companies and influence AI adoption at scale.
• Professional development budget and access to learning and conference opportunities.
• High-impact role shaping reusable AI infrastructure and engineering standards.
• Exposure to complex, real-world production AI systems rather than isolated projects.
• Collaborative environment with experienced engineers and cross-company technical leaders.
• Flexibility and autonomy in designing systems and defining best practices.