---
title: "Anand Kishore Jha - AI Engineer in Pune District | Tal"
description: "Meet Anand Kishore Jha, AI Engineer | Cut env setup 70% & built 5+ RAG chatbots | AWS Bedrock • Agentic AI • LLMs • Python based in Pune District. AI stack includes antigravity, claude code, and codex."
image: "https://tal.club/meet/anand-kishore-jha/share-image"
---

# Anand Kishore Jha on Tal

## Headline

AI Engineer \| Cut env setup 70% & built 5\+ RAG chatbots \| AWS Bedrock • Agentic AI • LLMs • Python

## About

AI/ML Engineer building production LLM, RAG and Agentic Systems on AWS Bedrock — 5\+ chatbots, 10K\+ daily queries, 99\.9% uptime\. Over the past \~3 years, I've shipped production\-grade AI systems that go beyond prototypes and actually move metrics: • Agentic AI infrastructure platform — autonomously selects architectures, generates Terraform IaC and orchestrates deployments on AWS\. Reduced environment setup from multiple days to \~20 minutes with built\-in retry logic \(up to 5 attempts\) and ghost\-resource cleanup\. • 5\+ production RAG chatbots — using LLMs, vector embeddings and AWS OpenSearch; serving 10K\+ daily queries \(90\-day avg\) with \~95% positive feedback/deflection and 99\.9% pipeline availability\. • Churn prediction with SHAP explainability — 86% recall on \~50K customers \(\~1M records\); SHAP\-driven insights informed a 15% cohort churn reduction \(3\-month post\-deployment cohort analysis\)\. • Amazon Nova semi\-finalist — Generative AI Advertising Pipeline combining text and image models on AWS Bedrock\. • Agent Safety Evaluation Pipeline — benchmarks LLM agent behavior across off\-topic rejection, jailbreak attempts and escalation handling using Pydantic for structured, schema\-validated scoring\. • Prompt\-Rubric Co\-Optimization Pipeline — iteratively co\-evolves prompts and evaluation rubrics to automate alignment between model outputs and task\-specific quality criteria\. What sets me apart: I have a bias for shipping\. When AWS releases a new ML service, I'm testing it the same week\. When a library doesn't do what I need, I extend it\. I think through multiple approaches before choosing the best one — and then I build it\. Tech stack: AWS \(Bedrock, OpenSearch, Lambda, SageMaker, ECS/EKS, EC2, S3\), Python, LLMs, RAG, Vector Embeddings, Prompt Engineering, AI Safety Evaluation, Pydantic, Docker, Kubernetes, Terraform, CI/CD, Scikit\-learn, TensorFlow, PyTorch, SHAP, MLOps, Claude Code, OpenAI Codex, Antigravity\. I'm looking for AI/ML Engineer or Generative AI Engineer roles where I can design and deploy cloud\-native AI systems that solve hard problems\. Open to connect — reach me at anand\.071972@gmail\.com

## Location

Pune District

## AI stack

antigravity

claude code

codex

hugging face

ollama

PydanticAI

## Work experience

AI Engineer at Mitchell Martin Inc\., Apr 2026 \- Present · 5 mos, Pune City, Working as a contract AI Engineer embedded with Exact Sciences \(acquired by Abbott Healthcare\) through Mitchell Martin, focused on production LLM safety and prompt optimization systems\. • Designed and implemented an Agent Safety Evaluation Pipeline to systematically benchmark LLM agent behavior across risk categories — off\-topic rejection, jailbreak attempts, and escalation handling — using Pydantic for structured, schema\-validated scoring outputs\. • Developing a Prompt\-Rubric Co\-Optimization Pipeline to iteratively co\-evolve prompts and evaluation rubrics, automating alignment between model outputs and task\-specific quality criteria\. • Leveraging Claude Code, OpenAI Codex, and Antigravity as AI\-assisted development tools to accelerate pipeline design and iteration\. Tech stack: Python, LLMs, Pydantic, Prompt Engineering, Claude Code, OpenAI Codex, Antigravity, LangChain, AI Safety Evaluation\.

Machine Learning Engineer at Quantiphi, Aug 2023 \- Mar 2026 · 2 yrs 8 mos, Mumbai, Design, build and deploy production\-grade ML systems on AWS\. • Architect and maintain an agentic AI infrastructure platform that automatically selects architectures, generates Terraform IaC and orchestrates deployments — reducing environment setup time by 70% \(days to \~20 minutes\) with built\-in retry logic \(up to 5 attempts\) and ghost\-resource cleanup\. • Deployed 5\+ production RAG chatbots using LLMs, vector embeddings and AWS OpenSearch; serving 10K\+ daily queries \(90\-day rolling avg\) with \~95% positive feedback/deflection rate and 99\.9% pipeline availability via CloudWatch Synthetics\. • Lead generative\-AI pipelines recognised as an Amazon Nova semi\-finalist, combining text and image models with AWS Bedrock\. • Deploy containerised ML services on AWS ECS/EKS using Docker, Kubernetes and CI/CD; design for high availability and auto\-scaling\. • Engineered a churn prediction model \(86% recall\) on \~50K customers \(\~1M records\); SHAP explainability highlighted actionable drivers that informed a 15% cohort churn reduction \(3\-month post\-deployment cohort analysis\)\. • Collaborate with cross\-functional teams \(product, DevOps, SRE, architects\) and mentor junior engineers\. Tech stack: AWS \(Bedrock, OpenSearch, Lambda, EC2, S3, SageMaker, ECS/EKS\), Python, LLMs, RAG, Vector Embeddings, Docker, Kubernetes, Terraform, CI/CD \(GitLab\), Scikit\-learn, TensorFlow, PyTorch, SHAP, MLOps\.

Machine Learning Intern at Quantiphi, Jan 2023 \- Jul 2023 · 7 mos, Mumbai, Maharashtra, India, Joined as an intern and quickly took ownership of ML workflows and annotation pipelines\. • Integrated LLMs and active‑learning techniques into annotation workflows, reducing manual labelling effort by 60%\+ and accelerating dataset creation\. • Implemented Git‑based version control and CI/CD pipelines across 4\+ teams, improving code quality, testing coverage and collaboration\. • Built an auto‑annotation pipeline using Python, OpenAI APIs and active learning to triage unlabelled data, speeding up model iterations\. • Established engineering best practices for ML workflow optimization, including code reviews, testing and documentation\. Tech stack: Python, LLMs, OpenAI APIs, Active Learning, Git, GitLab CI/CD, Docker, Data Labelling Tools\.

Software Development Intern at OpTAP Technology Consulting, Jun 2021 \- Apr 2022 · 11 mos, India, Developed data‑driven solutions for forecasting and automation in a consulting environment\. • Designed and implemented time‑series forecasting models \(ARIMA, Prophet\) that increased prediction accuracy by 30%, informing strategic inventory planning\. • Built a sales forecasting system using Python and Pandas; communicated insights to stakeholders and refined models based on feedback\. • Automated data‑entry workflows with OCR and Selenium, eliminating 100% of manual data entry and saving 20\+ hours/week for client reporting\. • Developed modular scripts and deployed them as micro‑services to integrate with client systems\. Tech stack: Python, Pandas, NumPy, Statsmodels, Prophet, Selenium, Tesseract OCR, OpenCV, Git\.

## Education

Bachelor of Engineering \- BE, Computer Science, CHANDIGARH UNIVERSITY

HSC \(12th\), Non\-Medical, KV NAD Karanja

[View the original profile](https://tal.club/meet/anand-kishore-jha)

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