Anthony Nikhil Reddy Lingala
Software Engineer · AI Engineer
AI Engineer @ CVS HealthEx-TagNTrac.aiEx-VerizonM.S. CS · NYU
I build production AI systems: RAG pipelines, LLM agents, and agentic infrastructure at scale. Currently at CVS Health engineering contract intelligence on GCP. Previously shipped distributed data platforms at Verizon and agentic AI systems at TagNTrac.ai.
3+
yrs experience
1,500+
EasyTex users
2×
AI Hackathon Winner
AWS Certified Developer – Associate
Amazon Web Services · Jul 2024
Experience
CVS Health
AI Engineer·Remote
- —Built RAG pipelines over contract PDFs on GCP using embedding models with semantic chunking and Vertex AI Vector Search, enabling natural language querying across thousands of contracts with sub-second response times.
- —Engineered LLM-powered SQL CASE statement generation from contract clauses using Vertex AI and schema-aware prompt engineering with output validation, automating rule extraction and saving 200+ analyst-hours per quarter.
- —Cut inference cost-per-request 35% and monthly spend by $12K via prompt compression, context window management, response caching, and batched inference on Cloud Run; built eval pipelines tracking accuracy and hallucination metrics.
GCPVertex AICloud RunCloud TraceRAGEmbeddingsLLMsEval Pipelines
TagNTrac.ai
Software Development Engineer – Generative AI & Platform·Remote
- —Architected production agentic AI systems on GCP with structured tool-calling, schema validation, and multi-step LLM workflows using Vertex AI and Gemini, serving 10K+ daily inference requests at 99.9% uptime via GKE.
- —Designed a deterministic LLM execution framework with MCP integration, idempotent retries, failure isolation, and Cloud Trace observability, reducing runtime errors 40% and enabling real-time cost-per-request monitoring.
- —Deployed Python-based RAG and agentic inference pipelines within customer cloud environments, integrating LLM models with internal APIs, legacy data, and enterprise security boundaries.
GCPVertex AIGeminiGKEMCPCloud TracePythonRAGLLM Agents
Verizon
🌟 Cheers for PeersSoftware Development Engineer·Hyderabad, India
- —Architected distributed cloud ingestion pipelines on AWS (EC2, SQS, SNS, Spark) processing large-scale telemetry and CI/CD data streams, improving data accuracy 25% and lowering operational costs 35% with Lambda + Step Functions serverless workflows.
- —Optimized Spark and Kafka streaming pipelines reducing end-to-end processing latency 65%, enabling near-real-time reporting for downstream BI consumers.
- —Served as DRI for production reliability across distributed systems, building CI/CD ingestion from 300+ jobs with Grafana/Prometheus observability dashboards that reduced MTTR.
AWSEC2LambdaStep FunctionsDynamoDBSparkKafkaGrafanaPrometheusPostgreSQL
Projects
1,500+
active users
10K+
PDFs generated
45%
latency reduced
- —Launched an AI-first platform architecting agentic document generation workflows that orchestrate pretrained models with external tools to generate deterministic PDFs — with eval pipelines tracking accuracy, latency, and cost-per-request.
- —Built LLM transformation pipelines with prompt engineering for structured rewriting and semantic normalization, deploying sandboxed infrastructure on GCP Cloud Run with caching and worker queues.
- —Implemented LLM tool calling for structured actions — bullet rewriting, skill extraction, job-description matching — reducing manual resume editing time by 60%.
GCPCloud RunLLMsReactNode.jsPython
Okada AI Hackathon
🏆 WinnerMulti-Agent Real Estate Platform
- —Built a multi-agent real estate AI system using Google ADK and LangGraph with ReAct patterns and hierarchical delegation, orchestrating specialized agents for property search, appointment booking, and bidirectional voice calling via Twilio STT/TTS.
- —Designed agent state management and granular tracing with MCP integration across multi-turn voice conversations, implementing self-reflection loops for response quality validation and reliable handoffs between agents.
- —Architected cost-efficient multi-agent deployment with dynamic task routing — Gemini for complex reasoning, lightweight models for routine classification.
Google ADKLangGraphMCPGeminiPythonTwilio
Skills
AI & Agentic
LLM Agents (Gemini, GPT)Google ADKMCPLangGraphMulti-Agent SystemsRAGPrompt EngineeringVertex AIEval PipelinesEmbeddings
Cloud & DevOps
GCP (Vertex AI, Cloud Run, Cloud Trace, BigQuery, GKE)AWS (Lambda, EC2, S3, SQS)DockerKubernetesCI/CDTerraform
Languages & Backend
Python (Flask, Django, FastAPI)JavaTypeScriptSQLGoC++Node.jsREST/GraphQL APIsMicroservicesAsync Processing
Data & Observability
PostgreSQLMongoDBRedisKafkaSparkVector DBs (Pinecone, Chroma, FAISS)PrometheusGrafanaDistributed TracingAirflow
Education
New York University
Sep 2023 – May 2025M.S. Computer Science·New York, NY·GPA: 3.89
Vellore Institute of Technology
Jul 2017 – Jun 2021B.Tech Computer Science·Vellore, India