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Tier-1 employer and Bangalore metro increase applicant density despite specialized AI-agent requirements.
Highly domain-specific ML/agent engineering with enterprise controls reduces cross-industry transferability.
Strict technical filters: explicit 7+ years, deep ADK/MCP and LLM/agent production experience required.
Design, develop, deploy, operate, and continuously improve production AI-native services, agentic systems, APIs, automation services, data pipelines, and cloud-native components.
Architect and govern agent-enabled AI workflows to improve delivery speed, code quality, and operational outcomes at scale, ensuring validation, security, resiliency, and reuse.
Own observability, monitoring, incident response, performance tuning, and cost optimization for AI and data engineering systems while leading design and code reviews and mentoring peers.
7+ years of applied software engineering experience with formal training or certification.
Proven hands-on experience architecting, developing, deploying, operating, and debugging production AI systems, agentic services, and cloud-native workloads.
Strong proficiency in Python and experience with AI/ML frameworks such as LangChain, LlamaIndex, PyTorch, or Hugging Face.
Deep experience with Google Agent Development Kit (ADK), Model Context Protocol (MCP), or equivalent agentic development frameworks including production operation and secure tool exposure.
Experienced in designing and leading adoption of AI-enabled development workflows incorporating human-in-the-loop validation, auditability, and secure data handling at scale.
Skilled in embedding enterprise knowledge into models and agents using vector stores, retrieval pipelines, and evidence-based response generation.
Capable of evaluating AI models and agents for task success, factuality, compliance, latency, cost, and operational health including monitoring for drift and anomalies.