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Tier-1 brand, popular ML role, and mid-level (3–6 year) experience drive high applicant competition.
Specialized ML/GenAI and agent orchestration requirements make cross-industry fit limited.
Multiple mandatory technical skills and a clear 4+ years requirement raise shortlisting strictness.
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Collaborate with senior engineers to design and develop scalable AI solutions using Large Language Models and agentic workflows.
Own end-to-end model lifecycle tasks including data preparation, training, deployment, and performance optimization in production environments.
Implement agent orchestration, observability frameworks, containerization (Docker/Kubernetes), and build APIs for AI service integrations.
Minimum 4 years of professional software engineering experience delivering AI/ML models to production.
Experience with both traditional ML tools (Scikit-learn/Pandas) and modern GenAI/LLM development.
Hands-on experience with agent orchestration frameworks such as LangGraph or AutoGen.
Proficiency in containerization technologies Docker and Kubernetes for scalable AI deployments.
Experienced in designing hybrid AI systems integrating both out-of-the-box and custom AI agents to solve multi-step business problems.
Skilled at implementing transparent AI workflows with deep tracing and logging to understand agent decisions.
Able to communicate complex AI concepts effectively to non-technical stakeholders and translate AI capabilities into business value.