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Abhinav Vengala
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
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LLM/agentic systems engineer who built a production "Agentic AI Diagnostic Assistant" for network engineers, using a multi-agent Llama 2 + LangChain architecture with RAG over telemetry/incident data in DynamoDB and confidence-based deferrals to reduce hallucinations. Also has strong MLOps/orchestration experience (Airflow, EventBridge, Spark, Docker, SageMaker/ECS) at multi-terabyte/day scale and delivered multilingual NLP analytics (fine-tuned BERT/spaCy) for support operations through hands-on stakeholder workshops.
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Key Strengths
- Built and deployed an agentic LLM diagnostic assistant for real-time network outage/root-cause analysis
- Strong grounding strategy using RAG over live telemetry + historical incidents with traceable sources
- Implemented confidence scoring with deferral to manual review to reduce hallucination risk and build SRE trust
- Designs measurable evaluation frameworks (precision/recall, latency, user trust) with modular unit testing per agent
- Orchestrated large-scale ML/data pipelines with Airflow + EventBridge; containerized stages with Docker on SageMaker/ECS
- Experience operating at high data scale (pipelines processing ~5TB telemetry/day)
- Effective cross-functional collaboration—translated NLP concepts for non-technical support ops via workshops and delivered multilingual ticket analytics
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