About

Mohit Kumar Dubey

Head of AI, ML & Engineering · Entermind Malaysia · Kuala Lumpur, Malaysia

I'm an AI and engineering leader with 16+ years building AI-first products and the organizations that ship them. I'm currently Head of AI, ML & Engineering at Entermind, a Kuala Lumpur consultancy serving enterprise and government clients across SEA and MENA, where I own the company-wide AI and engineering roadmap and lead an 11-engineer team across backend, AI/ML, frontend and QA.

Before Entermind I spent nearly a decade at VerSe Innovation (Dailyhunt / Josh / BuddyLoan), India's largest short-video and news platform at 350M+ users. I grew from Lead Software Engineer to Director of Engineering, leading a 35+ engineer organization across AI/ML, backend, mobile and web, and owning the AI/ML roadmap across content and lending. Earlier, I delivered enterprise and government mobility across the GCC and platform applications for SmartTV, Xbox and banking-grade security.

My work sits at the intersection of production GenAI delivery and engineering leadership: I own engineering P&L, design the operating model, manage executive stakeholders across the Gulf, SEA and the US, and still architect the systems myself. The projects below are framed as Problem, Approach, System and Outcome so the reasoning, not just the result, is visible.

Selected work

Projects

Problem, approach, system, and outcome for each, so the reasoning is visible, not just the result.

Entermind

Mindara, AI Employee for Enterprise Engagement

Visit

Problem: Enterprise engagement started with static intake forms that captured shallow requirements and lost nuance before sales ever engaged.
Approach: Replace the form with an always-on AI colleague that surfaces the real requirement and hands sales a structured brief.
System: Architected across seven capability levels (Level 1 in production, voice staged) on a LiteLLM gateway with Claude Opus / OpenAI / Gemini, LangGraph orchestration, Langfuse observability and ElevenLabs voice.
Outcome: 15% increase in solutioning lead calls.

LiteLLM
Claude Opus
Gemini
Langfuse
ElevenLabs
LangGraph

Entermind

Rukun Ready, Guardrail LLM for Policy Validation

Visit

Problem: Enterprise clients needed LLM outputs checked against national policy principles before deployment, with no off-the-shelf guardrail aligned to Malaysia's Rukun Negara.
Approach: Build a guardrail model that scores outputs for compliance and returns structured verdicts with safe rewrites.
System: Fine-tuned Qwen2.5-32B with LoRA/PEFT to emit structured JSON compliance verdicts, served on vLLM/RunPod, and released it open-source on HuggingFace.
Outcome: Firm's first open-source AI asset; 3K+ downloads; a standing pre-sales credibility anchor.

Entermind

AI Digital Twin, First Abu Dhabi Bank

Client · confidential

Problem: The Chief Credit Officer's calendar was the bottleneck for routine credit queries.
Approach: Give stakeholders 24/7 access to that expertise without adding to the CCO's meeting load.
System: Built a multimodal conversational twin over enterprise knowledge, with voice, memory and agentic workflows, on Azure AI Foundry, LangGraph, Tavus/ElevenLabs/Pipecat, pgvector, Redis and Azure AI Search.
Outcome: 20% lower routine meeting load and 40% better information availability.

Azure AI Foundry
LangGraph

Entermind

GEO Platform, GXS (Grab Group)

Client · confidential

Problem: Buyers had shifted discovery to AI assistants where the brand was largely invisible.
Approach: Make the brand measurably more visible and citable inside AI-driven discovery.
System: Built citation-aware analytics and optimization across four assistants (ChatGPT, Gemini, Claude, Perplexity) with competitor benchmarking on AWS Bedrock, model APIs, LangGraph and PostgreSQL.
Outcome: 45% lift in AI brand citation scores.

AWS Bedrock
OpenAI
Gemini

VerSe Innovation

BuddySmart, Conversational AI & Agentic RAG

Visit

Problem: Loan discovery was form-driven with heavy drop-off.
Approach: Turn discovery into a conversation that matches users to loans and actually converts.
System: Built a conversational matchmaking assistant with real-time eligibility checks and personalized offers on LangGraph/LangChain, Cohere embeddings, Qdrant, AWS Bedrock and ECS.
Outcome: 25% disbursal-conversion uplift and 20% retention gain.

VerSe Innovation

DailyTatva RAG v1 & v2

Visit

Problem: Personalized news retrieval at 350M+ users, with LLM spend growing faster than usage.
Approach: Hold retrieval quality high while bending the cost curve.
System: Built the v1 retrieval/inference foundation, then added model routing and response caching in v2 on LangGraph/LangChain, Gemini, Cohere embeddings, Vertex AI, Qdrant and ECS.
Outcome: 30–40% cut in LLM API cost while improving retrieval quality.

LangGraph

Skills

Technical skills

AI & ML: LLMs, RAG, Agentic RAG, Multi-Agent Systems, Fine-tuning (LoRA, PEFT), NLP, Recommendation Systems, Evaluation Harness, Agentic Loop design, Prompt Engineering, MLOps, Credit-Risk Models, Structured Outputs
Agent Frameworks: LangChain, LangGraph, CrewAI, DeepAgents
Training & Serving: PyTorch, TensorFlow, vLLM, SGLang, FastAPI
Model Platforms: AWS Bedrock, Amazon SageMaker, Vertex AI, Azure AI Foundry, OpenAI, Gemini, Claude, Cohere
Voice & Multimodal: ElevenLabs, Tavus, LiveKit, Pipecat, Daily, Serper, Tavily
Data & Vector Stores: PostgreSQL, pgvector, Qdrant, MongoDB, Chroma, FAISS, Redis
MLOps & Observability: Langfuse, Phoenix, Opik, LiteLLM, MLflow, Kubeflow, Airflow
Infrastructure: AWS, GCP, Docker, Kubernetes, ECS, EC2, CI/CD
Languages: Python, Pandas, NumPy, Pydantic, SQL, Swift

Background

Education & certifications

Education

Executive Post Graduate Programme in Machine Learning & AI (Specialization: Generative AI)
International Institute of Information Technology (IIIT), Bangalore
Nov 2024
B.Sc., Information Technology
Kanpur University, India
Mar 2009

Certifications

AWS Certified AI Practitioner
Responsible AI and Advanced Generative AI (DeepLearning.AI)
Mastering System Design
Software Architecture: Patterns for Developers
SOLID Principles in Software Design
REST-based Microservices API Development in Golang

Awards

Integrated Team Award · Team Excellence (×2) · Reward & Recognition (VerSe)

Languages

English, Hindi

Exposure

Malaysia, Singapore, UAE, India

← Back home

Mohit Kumar Dubey

Head of AI, ML & Engineering · Entermind Malaysia · Kuala Lumpur, Malaysia

Projects

Problem, approach, system, and outcome for each, so the reasoning is visible, not just the result.

Entermind

Mindara, AI Employee for Enterprise Engagement

Visit

Problem: Enterprise engagement started with static intake forms that captured shallow requirements and lost nuance before sales ever engaged.
Approach: Replace the form with an always-on AI colleague that surfaces the real requirement and hands sales a structured brief.
System: Architected across seven capability levels (Level 1 in production, voice staged) on a LiteLLM gateway with Claude Opus / OpenAI / Gemini, LangGraph orchestration, Langfuse observability and ElevenLabs voice.
Outcome: 15% increase in solutioning lead calls.

LiteLLM
Claude Opus
Gemini
Langfuse
ElevenLabs
LangGraph

Entermind

Rukun Ready, Guardrail LLM for Policy Validation

Visit

Problem: Enterprise clients needed LLM outputs checked against national policy principles before deployment, with no off-the-shelf guardrail aligned to Malaysia's Rukun Negara.
Approach: Build a guardrail model that scores outputs for compliance and returns structured verdicts with safe rewrites.
System: Fine-tuned Qwen2.5-32B with LoRA/PEFT to emit structured JSON compliance verdicts, served on vLLM/RunPod, and released it open-source on HuggingFace.
Outcome: Firm's first open-source AI asset; 3K+ downloads; a standing pre-sales credibility anchor.

Entermind

AI Digital Twin, First Abu Dhabi Bank

Client · confidential

Problem: The Chief Credit Officer's calendar was the bottleneck for routine credit queries.
Approach: Give stakeholders 24/7 access to that expertise without adding to the CCO's meeting load.
System: Built a multimodal conversational twin over enterprise knowledge, with voice, memory and agentic workflows, on Azure AI Foundry, LangGraph, Tavus/ElevenLabs/Pipecat, pgvector, Redis and Azure AI Search.
Outcome: 20% lower routine meeting load and 40% better information availability.

Azure AI Foundry
LangGraph

Entermind

GEO Platform, GXS (Grab Group)

Client · confidential

Problem: Buyers had shifted discovery to AI assistants where the brand was largely invisible.
Approach: Make the brand measurably more visible and citable inside AI-driven discovery.
System: Built citation-aware analytics and optimization across four assistants (ChatGPT, Gemini, Claude, Perplexity) with competitor benchmarking on AWS Bedrock, model APIs, LangGraph and PostgreSQL.
Outcome: 45% lift in AI brand citation scores.

AWS Bedrock
OpenAI
Gemini

VerSe Innovation

BuddySmart, Conversational AI & Agentic RAG

Visit

Problem: Loan discovery was form-driven with heavy drop-off.
Approach: Turn discovery into a conversation that matches users to loans and actually converts.
System: Built a conversational matchmaking assistant with real-time eligibility checks and personalized offers on LangGraph/LangChain, Cohere embeddings, Qdrant, AWS Bedrock and ECS.
Outcome: 25% disbursal-conversion uplift and 20% retention gain.

VerSe Innovation

DailyTatva RAG v1 & v2

Visit

Problem: Personalized news retrieval at 350M+ users, with LLM spend growing faster than usage.
Approach: Hold retrieval quality high while bending the cost curve.
System: Built the v1 retrieval/inference foundation, then added model routing and response caching in v2 on LangGraph/LangChain, Gemini, Cohere embeddings, Vertex AI, Qdrant and ECS.
Outcome: 30–40% cut in LLM API cost while improving retrieval quality.

LangGraph

Technical skills

AI & ML

LLMs, RAG, Agentic RAG, Multi-Agent Systems, Fine-tuning (LoRA, PEFT), NLP, Recommendation Systems, Evaluation Harness, Agentic Loop design, Prompt Engineering, MLOps, Credit-Risk Models, Structured Outputs

Agent Frameworks

LangChain, LangGraph, CrewAI, DeepAgents

Training & Serving

PyTorch, TensorFlow, vLLM, SGLang, FastAPI

Model Platforms

AWS Bedrock, Amazon SageMaker, Vertex AI, Azure AI Foundry, OpenAI, Gemini, Claude, Cohere

Voice & Multimodal

ElevenLabs, Tavus, LiveKit, Pipecat, Daily, Serper, Tavily

Data & Vector Stores

PostgreSQL, pgvector, Qdrant, MongoDB, Chroma, FAISS, Redis

MLOps & Observability

Langfuse, Phoenix, Opik, LiteLLM, MLflow, Kubeflow, Airflow

Infrastructure

AWS, GCP, Docker, Kubernetes, ECS, EC2, CI/CD

Languages

Python, Pandas, NumPy, Pydantic, SQL, Swift

Education & certifications

Education

Executive Post Graduate Programme in Machine Learning & AI (Specialization: Generative AI)
International Institute of Information Technology (IIIT), Bangalore
Nov 2024
B.Sc., Information Technology
Kanpur University, India
Mar 2009

Certifications

AWS Certified AI Practitioner
Responsible AI and Advanced Generative AI (DeepLearning.AI)
Mastering System Design
Software Architecture: Patterns for Developers
SOLID Principles in Software Design
REST-based Microservices API Development in Golang

Awards

Integrated Team Award · Team Excellence (×2) · Reward & Recognition (VerSe)

Languages

English, Hindi

Exposure

Malaysia, Singapore, UAE, India