Lead AI Engineer
Fa Ewmy Saasfaprod1 Open · verified Oct 22, 2026 At a glance
Senior AI Engineering role at Fa Ewmy Saasfaprod1. Hyderabad · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Design production AI applications and connect agent-based systems with existing software
- Build and monitor LLM workflows that use multiple agents, orchestration, RAG, prompts and contextual memory
- Create agents that plan and carry out multi-step tasks, using MCP to structure exchanges with tools and systems
- Measure AI accuracy, safety and performance, then improve workflow speed, cost and scalability
- Guide developers, set engineering practices and work with product, data and software teams on AI features
What you bring
- Six to eight years in software development, including mentoring or team leadership experience
- At least two years building applications with generative AI or agent-based systems
- Strong Python and backend skills, with experience designing REST APIs and scalable services
- Hands-on work with LLM APIs, embeddings, vector search or RAG, plus familiarity with agent frameworks
- Knowledge of responsible AI governance and experience with system design and performance tuning
Who this fits
This senior role suits a software engineer who can lead architecture and mentor other developers while building AI applications. It is based in Hyderabad and follows a hybrid work arrangement.
From the employer
You will design scalable AI workflows, integrate autonomous agents into real-world applications, and help translate cutting-edge AI capabilities into reliable business solutions.
• Architect and Design production-ready applications leveraging Generative AI and agentic AI frameworks.
• Build, Deploy and Monitor intelligent workflows using LLMs, multi-agent coordination, and orchestration pipelines.
• Integrate new AI applications with traditional pre-existing applications.
• Implement prompt engineering strategies, retrieval-augmented generation (RAG), and contextual memory systems.
• Implement various AI coding techniques (context-driven, spec-driven development etc.).
• Architect and implement AI agents capable of reasoning, planning, and multi-step task execution.
• Implement and utilize Model Context Protocol (MCP) patterns to enable structured communication between agents, tools, and systems.
• Provide mentoring and technical guidance to developers and other AI Engineers.
• Develop evaluation pipelines to measure accuracy, safety, and performance of AI systems.
• Optimize latency, cost efficiency, and scalability of AI-powered workflows.
• Collaborate with product managers, data teams, and software engineers to deliver AI features.
• Ensure reliability through testing, logging, monitoring, and observability of AI behavior.
• Own architectural decisions, coding standards, and best practices.
• Act as an AI trailblazer for development teams by promoting best practices, reusable patterns, and adopting AI-driven engineering approaches.
• Apply AI governance principles to ensure responsible model usage, auditability, and transparency in AI workflows.
• Support implementation of governance controls related to data handling, model behavior monitoring, and risk mitigation.
• Research emerging AI tools and frameworks to continuously improve system capabilities.
• Document AI workflows and maintain reproducible engineering processes.
• 6-8 years of professional software development experience (Dotnet/React/Full-stack) with mentorship/team leadership experience.
• Minimum 2 years of hands-on experience building applications using Generative AI or agentic AI systems.
• Strong proficiency in Python and backend engineering principles.
• Experience designing RESTful APIs and scalable service architectures.
• Practical experience with LLM APIs, embeddings, vector search, or RAG pipelines.
• Working knowledge of AI governance principles, responsible AI practices, or compliance considerations.
• Familiarity with agent frameworks and autonomous decision workflows.
• Understanding of data structures, system design, and performance optimization.
• Strong analytical thinking and problem-solving skills.
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Preferred Qualifications
• Experience deploying AI applications in cloud environments (preferably AWS).
• Familiarity with vector databases and knowledge retrieval architectures.
• Experience working with Git, CI/CD pipelines, and production deployments.
• Exposure to containerization and orchestration tools (Docker, Kubernetes).
• Experience implementing evaluation or monitoring pipelines for AI agents.
• Knowledge of AI safety, guardrails, and responsible AI practices.
• Knowledge of distributed systems or event-driven architecture.