Senior AI Engineer

Overview

Design, build and operationalize enterprise AI solutions, covering AI engineering, foundation models, context engineering, retrieval architectures, agentic systems and LLMOps, and articulate their business impact.

What you'll do

• Collaborate with stakeholders to understand strategic objectives and identify opportunities to leverage data and data quality for enabling AI and machine learning.
• Be responsible for overall AI system design, problem framing and governance, identifying and managing risks and issues.
• Design, develop and implement AI platforms that drive measurable business results.
• Design experiments for the team to rapidly test ideas and assumptions, and identify future courses of action.
• Use quality metrics to evaluate and deploy agents that will be deployed in the production environment.
• Communicate technical findings and insights to stakeholders in a clear, easy and concise way.
• Contribute to the growth of the AI Engineer community and promote a culture of data literacy within the organization.
• Address ethical concerns and adhere to ethical guidelines using FATTER AI (fairness, accountability, transparency, trustworthiness, explainability, responsibility).
• Work with LLMOps practices and data versioning tools.

What you'll need

• Experience designing and deploying AI solutions using enterprise AI platforms such as AWS AgentCore, Google Vertex AI, Microsoft Foundry or equivalent cloud AI platforms.
• Strong experience in one of these languages: Python, Java, or TypeScript / JavaScript.
• Experience utilizing and building production grade code with at least one AI framework such as Langchain, Langgraph, Pydantic, MS Agent Framework, AWS Strands, or Google ADK.
• Experience building interoperable AI solutions using emerging standards such as Model Context Protocol (MCP), Agent-to-Agent (A2A), AG-UI and similar protocols, enabling integration, communication and collaboration across AI agents, enterprise platforms and external tools.
• Establish standards for context engineering, prompt engineering and retrieval strategies that enable reliable, scalable and trustworthy AI systems across products.
• Architect enterprise agentic systems, defining reusable patterns for orchestration, tool integration, reasoning, resilience and multi-agent collaboration.
• Define retrieval and knowledge platform architectures while establishing LLMOps standards including AI evaluation frameworks, observability, guardrails, Responsible AI, Explainable AI, governance and continuous improvement.
• Ensure compliance with data privacy regulations and ethical standards when handling sensitive information.
• Understand the importance of stakeholder management and can liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust.
• Be resilient in ambiguous situations and able to adapt your role to approach challenges from multiple perspectives.
• Not shy away from risks or conflicts, and skillfully manage them.
• Be eager to coach, mentor and motivate others, and influence teammates to take positive action and accountability for their work.
• Enjoy influencing others and advocate for technical excellence while being open to change when needed.
• Be a proven leader with a track record of encouraging teammates in their professional development and relationships.
• Cultivate strong partnerships and understand how relationship building can bring new opportunities to the business.

Details

• Location: Bangalore, Chennai, Hyderabad or Pune, India.
• Work mode: Remote or hybrid.