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Google is an American multinational technology company specializing in Internet-related services and products. These include online advertising technologies, search, cloud computing, software, and hardware.
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Machine Learning (ML)Responsibilities
- Engineering technical lead for the AI Garage team which responsible for developing AI/ML systems and Agentic solutions in HR domain
- Drive engineering excellence for the AI Garage team
- Provide technical leadership in regards to immediate and complex technical problems to leads of associated teams.
- Influence and Design and build scalable Agentic systems in HR domain.
- Collaborate with AI Garage Leads ( Eng Managers, Product Managers and AI strategist ) and stakeholders to understand their needs and translate them into technical solutions.
- Stay up-to-date with the latest trends in Agentic and AIML engineering.
Required Skill :
- Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience
- 8+ years of experience in software development, including proficiency in one or more programming languages, artificial intelligence, machine learning algorithms and tools, and Generative AI (Large Language Models, Multi-Model, etc.)
- Design and implement scalable AI solutions for Enterprises.Experience in Building and rolling out AI and agentic solutions for Global Enterprises
- Multi-Agent Systems: Handle collaboration, memory persistence, and dynamic planning using frameworks like LangGraph, CrewAI, or AutoGen.
- Optimization & Evaluation: Track agent trajectories, manage cost/latency, and implement LLM-as-a-judge frameworks (e.g., Phoenix, LangSmith).
- Safety & Compliance: Prompt injection mitigation, sandboxed code execution, and human-in-the-loop (HITL) checkpoints.
- Experience with offline,online and RL based validation
- Metrics Selection: Knowing which evaluation metric matches specific business constraints (e.g., using Precision/Recall or Area Under the ROC Curve (AUC) rather than basic Accuracy on an imbalanced classification dataset).
- Good understanding of the Bias-Variance tradeoff, Overfitting vs. Underfitting, and practical techniques to fix them (L1/L2 Regularization, Dropout, Early Stopping).
- Dynamic Context Compaction: Skills in designing algorithms that programmatically truncate, summarize, or extract semantic milestones from a running conversation log so the agent doesn't suffer from "lost in the middle" phenomena during long-horizon tasks.
- Advanced Reasoning Frameworks: Implementing and customizing structural execution patterns like ReAct (Reason + Act), Tree of Thoughts (ToT), and Plan-and-Solve loops, giving models the cognitive scaffolding to break down vague goals into deterministic DAGs
- Evidence in runtime layers that scan user inputs for prompt injection or jailbreak attempts designed to hijack an agent’s system tools, alongside strictly isolating tool execution environments
- Experience with LoRA or SFT and reasoning
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