AI ENGINEER ( Full-time at a Fortune 500 tech MNC)

Role Overview

AI Engineer

Key Responsibilities

• Design and Build Agentic AI Systems:

• Develop autonomous AI agents capable of planning, reasoning, and executing complex, multi-step tasks across different systems with minimal human intervention.

• Automate Business Workflows:

• Identify and automate repetitive, time-consuming business processes using AI agents, freeing up human resources for strategic work.

• Enhance Developer Efficiency:

• Create AI-powered tools and agents that assist developers across the software development lifecycle (SDLC), including code generation, refactoring, debugging, and documentation.

• Improve Business KPIs:

• Collaborate with stakeholders to identify critical performance indicators and design AI solutions that improve revenue, reduce costs, and enhance customer satisfaction.

• Integrate AI Agents with Enterprise Systems:

• Develop and manage APIs and integrations for AI agents to interact with company platforms, including databases, collaboration tools, and existing software.

• Orchestrate Multi-Agent Workflows:

• Utilize frameworks to coordinate multiple AI agents in collaborative workflows to solve larger business challenges.

• Maintain Ethical and Safety Standards:

• Implement guardrails and monitoring to ensure AI agents operate responsibly, securely, and adhere to ethical guidelines.

• Conduct Research and Stay Current:

• Keep up with advancements in agentic AI, LLMs, and development practices. Apply new knowledge to improve solutions and identify opportunities for AI implementation.

Experience & Skills Required

• Overall Experience:

  7–8 years, with

  4+ years in AI/ML software development

  focusing on autonomous/agentic systems.

• Programming:

  Strong proficiency in

  Python

  and AI/ML libraries such as

  PyTorch, TensorFlow, Hugging Face

  .

• Agentic Frameworks:

  Hands-on experience with

  AI agent orchestration frameworks

  .

• API & Integration:

  Solid experience in

  API development

  (FastAPI, Flask) and integrating diverse systems.

• Cloud Platforms:

  Experience with

  AWS, Azure, or GCP

  for AI deployment.

• Databases:

  Proficient in

  SQL

  and

  vector databases

  (e.g., Pinecone, Chroma) for retrieval-augmented generation (RAG) and memory systems.

• Soft Skills:

  Strong problem-solving abilities, excellent communication for cross-functional collaboration, and the ability to translate technical concepts for non-technical stakeholders.