---
title: "Nishant Nischaya - Senior AI/ML Engineer (Specialist Programmer L2) — Generative AI & LLM Systems in Pune City | Tal"
description: "Meet Nishant Nischaya, Senior AI/ML Engineer (Serving Notice Period) specialising in Generative AI solutions | RAG | Agentic AI | LLMOps | Fine-Tuning based in Pune City. AI stack includes antigravity and codex."
image: "https://tal.club/meet/nishant-nischaya/share-image"
---

# Nishant Nischaya on Tal

## Headline

Senior AI/ML Engineer \(Serving Notice Period\) specialising in Generative AI solutions \| RAG \| Agentic AI \| LLMOps \| Fine\-Tuning

## About

Senior AI/ML Engineer with 5 years 8 months building production Generative AI and Agentic AI systems for Apple Inc\. and CVS Healthcare \(Aetna\) — at Infosys\. I design and ship systems that are production ready and scalable\. RAG pipelines with hybrid retrieval \(BM25 \+ semantic, RRF re\-ranking\), multi\-agent orchestration with LangChain/LangGraph/MCP, fine\-tuned open\-weight LLMs \(Llama\-3\.2\-3B via LoRA/QLoRA on Unsloth\), and Azure\-native LLMOps \(MLflow, LangSmith, Docker, Kubernetes, CI/CD\)\. What I've delivered: → Architected and built an enterprise\-grade multi\-agent SRE Copilot platform \(LangGraph orchestration, Mem0 persistent memory, MCP\-based tool integration into Grafana/Loki/Mimir/Tempo/Splunk, Agentic RAG over Confluence, NeMo Guardrails for AI safety, LangFuse/ClickHouse observability\) enabling conversational incident diagnosis across 5\+ enterprise observability platforms at CVS Healthcare/Aetna\. → Architected a 3\-tiered LLM allocation \+ data\-egress\-prevention layer that cut LLM operational costs 40\-50% while scaling from 3 to 150\+ applications at CVS Healthcare/Aetna\. → Built a codebase\-vectorization RAG pipeline \(FAISS, MongoDB Atlas Vector, Azure AI Search\) that cut LLM token consumption \~60% across 15\+ repos at Apple's Emerging Technologies & Services org — adopted by 30\+ engineers\. → Shipped LLM\-powered test generation tooling \(LangChain \+ custom MCP servers\) handling 500\+ daily specification requests, cutting manual authoring time \~70% across 20\+ modules\. → Built failure\-classification ML models \(Scikit\-learn\) hitting \~88% accuracy across 10\+ failure categories, reducing mean\-time\-to\-triage \~35%\. Core stack: Python · LLMs · RAG · Agentic AI \(ReAct, planning, router\-specialist, tool\-calling patterns\) · LangChain · LangGraph · MCP · Fine\-Tuning \(LoRA/QLoRA/PEFT\) · HuggingFace · PyTorch · FAISS · Azure AI Search · pgvector · Azure ML · Azure OpenAI · Docker · Kubernetes · MLflow · LangSmith · FastAPI · Responsible AI & Guardrails\. Recognized 3x as Platinum Talent at Infosys, with Outstanding performance ratings in 4 of the last 5 years\. Currently exploring Senior/Staff AI\-ML Engineer and Generative AI Engineer roles where I can take production LLM systems from prototype to scale\. Open to connecting with fellow GenAI builders, recruiters, and hiring managers\.

## Location

Pune City

## AI stack

antigravity

codex

## Work experience

Senior AI/ML Engineer \(Specialist Programmer L2\) — Generative AI & LLM Systems at Infosys, Jun 2023 \- Present · 3 yrs 3 mos, Pune District, Delivering Agentic AI systems for CVS Healthcare’s Aetna Insights Observability Platform\. SRE COPILOT — MULTI\-AGENT AI PLATFORM FOR INCIDENT DIAGNOSIS & OBSERVABILITY • Architected a multi\-agent SRE Copilot using LangGraph to orchestrate specialized agents for conversational incident diagnosis and failure investigation\. • Implemented persistent memory \(Mem0\) for cross\-session context retention; built Redis\-based prompt caching to reduce latency\. • Integrated MCP\-based tool connectivity to Loki, Grafana, Mimir, Tempo, and Splunk for real\-time telemetry retrieval within agent workflows\. • Built an Agentic RAG architecture over Confluence for context\-aware operational guidance, served via Azure AI Foundry/Azure OpenAI\. • Built observability pipelines \(LangFuse, ClickHouse, Redis\) for prompt tracing, token analytics, and audit logging\. • Designed AI safety guardrails \(NVIDIA NeMo Guardrails\) against prompt injection, jailbreaks, and data exfiltration; ran adversarial red\-teaming\. • Built evaluation pipelines for agent/RAG faithfulness, groundedness, and tool\-selection accuracy\. Impact: unified incident diagnosis across 5\+ observability platforms with a production multi\-agent architecture, reducing MTTR\. LLM COST OPTIMIZATION & DATA EGRESS PREVENTION • Architected a 3\-tiered LLM allocation pattern \(self\-hosted Qwen3\-Coder:30B, GPT\-3\.5\-turbo, gated GPT\-4\-class endpoints\) behind a zero\-trust perimeter — cut LLM costs 40–50% while scaling from 3 to 150\+ applications\. • Fine\-tuned Llama\-3\.2\-3B \(LoRA/QLoRA, Unsloth\) as a routing/classification/redaction engine using 1,500 semantic prototypes from K\-Means clustering over 50K production logs\. • Established an LLMOps observability pipeline \(MLflow, LangSmith\) running asynchronously to keep latency minimal\. • Containerized with Docker/Kubernetes on Azure; CI/CD via GitHub Actions\.

Specialist Programmer \- Strategic Technology Group at Infosys, Jan 2022 \- May 2023 · 1 yr 5 mos, Trivandrum, Delivering Agentic AI systems for Apple’s Emerging Technologies & Services \(ETS\) org\. AGENTIC TEST AUTOMATION & MCP INFRASTRUCTURE • Built custom MCP servers using generative AI for automatic Gauge specification generation from natural\-language prompts, handling 500\+ daily specification requests at peak load\. • Built an LLM\-powered test case/test data generation tool \(LangChain\), reducing manual specification authoring time \~70% across 20\+ application modules; monitored via LangSmith\. RAG PIPELINE & CODEBASE INTELLIGENCE • Architected a codebase\-vectorization RAG pipeline \(AST\-aware chunking, HuggingFace all\-MiniLM, FAISS, MongoDB Atlas Vector, Azure AI Search\) with hybrid BM25 \+ semantic retrieval and RRF re\-ranking\. • Cut LLM token consumption \~60% across 15\+ active repositories; added Responsible AI guardrails and metadata enrichment\. • Built agentic AI chat interfaces \(LangChain\) for natural\-language data retrieval from REST APIs and internal databases, adopted by 30\+ engineers\. DEPLOYMENT & INFRASTRUCTURE • Deployed production AI services \(FastAPI, Flask\) on Azure, containerized with Docker, orchestrated via Kubernetes, automated through Jenkins and GitHub Actions CI/CD — integrated with Azure ML and Azure OpenAI\. • Applied prompt and context engineering to maintain sub\-3\-second response times at production load\.

Digital Specialist Engineer \- Strategic Technology Group at Infosys, Mar 2021 \- Dec 2021 · 10 mos, Trivandrum, Infrastructure Demand Forecasting for Capacity Planning 100\+ app teams on CVS's observability platform had no reliable method to anticipate infrastructure demand during high\-traffic periods — risking budget waste or service degradation during Open Enrollment\. • Designed and deployed an ML time series forecasting pipeline using SARIMA/SARIMAX to predict per\-app infrastructure usage across 100\+ applications, incorporating enrollment calendar regressors and seasonal demand patterns\. • Delivered app\-level forecasts replacing ad\-hoc estimation with model\-driven capacity planning, giving engineering teams ahead\-of\-time signals for resource allocation and de\-allocation decisions ■ Adopted across full 100\+ app portfolio · Directly informed infrastructure budgeting for a Fortune 500 healthcare client during their highest\-stakes operational peri Failure Intelligence & ML Models Large\-scale automated test executions generated hundreds of recurring failures daily with limited intelligent grouping or prioritization — increasing triage effort, slowing RCA workflows, and delaying engineering response times\. • Developed predictive classification models using Scikit\-learn to automatically categorize failures across 10\+ failure types, achieving \~88% accuracy and reducing mean time to triage by \~35% — enabling optimized task assignment workflows • Implemented ML clustering algorithms for bulk failure analysis across 200\+ daily test runs, identifying similar failure patterns across builds and cutting critical failure investigation time by \~40%\. ■ \~88% classification accuracy across 10\+ failure types · \~35% reduction in mean triage time · \~40% faster critical failure investigation across 200\+ daily test runs

SDE Intern at Tech Mahindra, Jun 2019 \- Aug 2019 · 3 mos, Noida Area, India, Developed UI and Validation tier elements using Angular and Java for e\-TMR portal of the Government of India where users can file for patents and trademark registration\.

Internship Trainee at i3indya Technologies, Jun 2018 \- Jul 2018 · 2 mos, Delhi, Trained in Embedded systems \(PIC microcontroller\) and IoT platform\.

## Education

Bachelor of Technology \- BTech, Electrical, Electronics and Communications Engineering, Maharaja Agrasen Institute Of Technology, Delhi

A\.G\.D\.A\.V\.

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