Principal Engineer, Agentic AI

REQUIREMENTS:

• Total experience 11+ years.
• Should have experience in software engineering, with strong depth in Python.
• Strong expertise in Machine Learning, Deep Learning, and statistical modeling
• Experience designing scalable ML systems and production pipelines
• Hands-on experience with agentic frameworks (LangGraph, CrewAI, AutoGen) and multi-agent orchestration
• Should have proven experience architecting and delivering production-grade Generative AI applications at scale.
• Should have deep understanding of LLM integration patterns, RAG systems, and AI-driven UX design.
• Should have strong system design skills across backend, frontend, and AI infrastructure layers.
• Must have experience defining technical strategy and influencing architecture across teams or pods.
• Must have experience with microservices, APIs, and scalable backend systems.
• Strong experience in MLOps practices including CI/CD, model versioning, monitoring, and governance.
• Hands-on experience with tools such as MLflow, Vertex AI, Kubeflow, or similar.
• Should have deep experience with cloud platforms, especially GCP (Vertex AI, BigQuery) and/or Databricks
• Should have strong grasp of security, privacy, and governance considerations for enterprise AI.
• Must have ability to translate ambiguous business problems into durable technical architectures.
• Should have excellent communication skills, with the ability to influence senior stakeholders and technical leadership.

RESPONSIBILITIES:

• Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
• Design end-to-end AI/ML architectures, including scalable, secure, and production-ready systems using Machine Learning, Deep Learning, and Large Language Models (LLMs). Establish best practices for building robust and reusable AI platforms.
• Design scalable, secure, and cost-efficient backend platforms for LLM inference, RAG pipelines, and agent-based orchestration.
• Lead the design and implementation of complex GenAI workflows that combine LLMs, tools, APIs, structured data, and user context.
• Establish engineering standards and best practices for prompt design, model integration, evaluation, and observability.
• Drive GenAI platformisation—building reusable components, SDKs, and frameworks used across multiple teams or products.
• Partner with product, design, data, and business leaders to translate strategic objectives into scalable technical solutions.
• Review critical designs and codebases, unblock teams on complex technical challenges, and raise the overall engineering bar.
• Lead technical discovery and solutioning for high-impact initiatives, including client or executive-facing workshops when required.
• Ensure enterprise readiness: security, privacy, compliance, governance, and responsible AI practices.
•  Use AI-assisted development tools (e.g., Copilot, Claude Code) to accelerate delivery while maintaining production-grade quality.
• Mapping decisions with requirements and be able to translate the same to developers.
• Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.
• Defining guidelines and benchmarks for NFR considerations during project implementation
• Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers
• Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
• Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it
• Understanding and relating technology integration scenarios and applying these learnings in projects
• Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
• Carrying out POCs to make sure that suggested design/technologies meet the requirements.

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.