anindya@systems:~$ whoami
Anindya Majumder
anindya@systems:~$ role --current
AI & Automation Engineer
anindya@systems:~$ echo "$MISSION"
I build intelligent systems for in-house and production use that are optimized and scalable.
anindya@systems:~$ echo "$STACK"
Agentic AI | RAG Pipelines | Backend Engineering | CI/CD | Workflow Orchestration | Cloud (AWS, VPS)
anindya@systems:~$ arch --stack --view layers[01.DATABASE]
classical_db = PostgreSQL | MySQL | MongoDB
vector_db = ChromaDB | FAISS | Pinecone
graph_db = Neo4j | AstraDB
[02.AI_ML]
frameworks = LangChain | LangGraph | ADK | CrewAI
libraries = PyTorch | OpenCV | Ray Serve
[03.AUTOMATIONS]
workflow_orchestration = n8n | Make | Zapier
scrapers = Scrapy | Playwright
crawlers = Crawl4AI | Beautiful Soup
[04.BACKEND]
frameworks = FastAPI | Spring Boot
protocols = REST | GraphQL | JWT | WebSocket
languages = Python | Java
[05.DEVOPS]
pipelines = GitHub Actions | Automated CI/CD
runtime = Docker | Kubernetes | Nginx
infra = AWS | VPSanindya@systems:~$ cat /etc/anindya/core.configprofile:
name: Anindya Majumder
role: AI Workflow Automation Engineer
current: RiseUp Labs Ltd.
location: Dhaka, Bangladesh
status: active
operating_mode:
default: production-first
mindset:
- systems_thinking
- outcome_over_hype
- iterate_fast_measure_faster
constraints:
- latency_budget_awareness
- token_cost_discipline
- reliability_before_scale
engineering_policy:
architecture_rules:
- design_for_observability
- fail_gracefully_and_recover
- secure_tool_boundaries
delivery_rules:
- test_before_release
- automate_repeatable_step
- keep_rollbacks_ready
current_focus:
- agentic systems design and orchestration
- scalable AI and backend architecture
- reliable production delivery and observability
open_to:
- research_collaboration
- backend_and_ai_roles
- open_source_projectsanindya@systems:~$ impact --scope production --format log@@ impact.01 :: Token-Efficient LLM Routing @@
+ method: prompt caching + semantic reranking + model routing
+ result: lower token burn in production traffic
@@ impact.02 :: MCP connectivity layer @@
+ method: custom MCP servers for native AI-to-API access
+ result: secure and fast tool-connected agent workflows
@@ impact.03 :: Hallucination-Resistant Legal RAG @@
+ method: Graph-RAG + reranker pipeline
+ result: non-hallucinated retrieval quality for legal chatbot
@@ impact.04 :: NLQ over PostgreSQL @@
+ method: schema-aware retrieval with pgvector + live progress tracking
+ result: faster structured, user-level analytics using natural language
@@ impact.05 :: Instruction Fidelity Optimization @@
+ method: RLHF + DPO + GRPO
+ result: stronger instruction following and adapt user behavior
@@ impact.06 :: Low-VRAM Adaptation Pipeline @@
+ method: LoRA + QLoRA in constrained compute env
+ result: lower VRAM adaptation with faster iteration cycles
@@ impact.07 :: CI/CD automation @@
+ method: GitHub Actions + containerized build/test/deploy
+ result: faster and repeatable release pipelines
@@ impact.08 :: Latency-Optimized Service Architecture @@
+ method: layered caching + async job queues + read-replica routing
+ result: lower p95 latency and higher throughput under peak load
@@ impact.09 :: Zero-Downtime Delivery Strategy @@
+ method: blue-green deployment + canary checks + infrastructure drift detection
+ result: near-zero downtime releases with safer rollbacks and lower operations overhead
@@ impact.10 :: Modular Architecture & Type Safety @@
+ method: dependancy injection + design patterns (repository, factory, adapter, strategy, observer)
+ result: faster onboarding, reduced bug surface, testable and scalable servicecredentials.log
anindya@systems:~$ credentials --list --format toml[[certifications]]
id = "CERT.01"
title = "LangChain for LLM Application Development (Skill Track)"
provider = "DataCamp"
focus = ["Production-ready RAG", "Agentic workflows"]
[[certifications]]
id = "CERT.02"
title = "Machine Learning Specialization"
provider = "Stanford University / Coursera"
focus = ["Supervised learning", "Neural networks", "Unsupervised learning"]
[[certifications]]
id = "CERT.03"
title = "Containerization and Virtualization (Skill Track)"
provider = "DataCamp"
focus = ["Docker", "Docker Compose", "Kubernetes", "Orchestration"]
[[awards]]
id = "AWARD.01"
event = "Machine Learning Olympiad 2024 (GDG)"
status = "Rank #39"
[[awards]]
id = "AWARD.02"
event = "IEEE CS Spectrum: Code Crash 2023"
status = "Finalist (Top 9)"
[[awards]]
id = "AWARD.03"
event = "University Rover Challenge 2023 (NASA / Mars Society)"
status = "Ranked 2nd in Asia"anindya@systems:~$ echo "Let's build something impactful"