AI Deploy Network
Bikash Singhal

AI Deploy Network / Builder

Bikash Singhal

AI Engineer | Python • Agentic AI • RAG • LLMs • AWS | Former Staff Engineer @ Qualcomm

IndiaRaipurSeniorAvailable Now

Published evidence

2 AI Deployments

2 publicly identified

01

About Bikash

Senior Software & AI Engineer with 10+ years of experience, including Staff/Lead-level engineering at Qualcomm and work on consumer software reaching 10M+ Android downloads. I design and build production-oriented AI systems end-to-end—from requirements and architecture through implementation, evaluation, deployment, and production debugging. My hands-on work includes RAG, agentic AI, LLM applications, LangGraph, MCP, Python, FastAPI, AWS, Docker, hybrid retrieval, reranking, and AI evaluation. I built and deployed RAG Engine and Negotia, with GitHub repositories and live demos available.

Professional profile

Location
Raipur, India
Work mode
Hybrid, Remote
Availability
Available Now
Industry experience
10+ years
Languages
English, Hindi, Odia

02

Proven on the network

Implementation records and publicly available work, where published.

2 AI Deployments delivered, including 2 publicly identified implementations available to inspect.

03

What Bikash builds

Implementation domains listed on this profile. Evidence-supported items are identified separately below.

Specialisations

Agentic Workflow Automation

AI Evaluation & Testing

Multi-Agent Systems

RAG & Knowledge Retrieval

Semantic Search

Builder-listed deployment types

  • AI Agent
  • AI Evaluation Framework
  • Knowledge Assistant
  • MCP Server
  • Multi-Agent Systems
  • RAG System

04

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

Negotia – Agentic AI Negotiation Coach with Adaptive Memory

Agentic AI negotiation coaching platform with scenario generation, adaptive AI opponents, real-time conversations, structured feedback, cross-session memory, and personalized strategies. Built with LangGraph, FastAPI, React, PostgreSQL, and Amazon Be

Software & SaaS · AI Agent · AI Evaluation Framework · MCP Server · Multi-Agent Systems

View Deployment
02 / AI DeploymentPublished record

RAG Engine with Hybrid Retrieval, Reranking & Evaluation

Production-oriented RAG system combining hybrid FAISS + BM25 retrieval, query expansion, cross-encoder reranking, systematic evaluation, Amazon Bedrock LLM inference, FastAPI APIs and AWS deployment.

Software & SaaS · AI Evaluation Framework · Knowledge Assistant · RAG System

View Deployment

05

Implementation capabilities

Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.

Evidence-supported

  • Multi-Agent Systems1 record
  • AI Agent1 record
  • AI Evaluation Framework2 records
  • Knowledge Assistant1 record
  • MCP Server1 record
  • RAG System1 record

Builder declared

  • Agentic Workflow Automation
  • AI Evaluation & Testing
  • RAG & Knowledge Retrieval
  • Semantic Search

06

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

  • Amazon Bedrock2 records
  • Docker2 records
  • FAISS1 record
  • FastAPI2 records
  • Hugging Face Inference1 record
  • LangGraph1 record
  • Model Context Protocol1 record
  • PostgreSQL1 record
  • Python2 records
  • Ragas1 record
  • pytest2 records

Builder declared

  • MongoDB

07

Industry experience

Published Deployments support the listed counts. Other industries are Builder declared.

Software & SaaS

2 published Deployments

Cloud & Infrastructure Providers

Builder declared

IT Services & Systems Integration

Builder declared

Telecommunications

Builder declared

08

Trust & verification

A closer look at how this profile and its evidence are reviewed.

Community Member

Published evidence is reviewed against the AI Deploy Network Evidence Standard.

How verification works
Community

Verified Builder

Independently reviewed by AI Deploy Labs against the platform Evidence Standard.

How this is verifiedWhat the platform has established, and what remains builder-declared.
Identity and organisation
Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
AI Deployments
An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
Business outcomes
Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
Evidence
Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
Currency
Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
Traceability
Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.

The platform does not independently audit client systems or financial records. How this is verified

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