Hire the Best Python Consultants in Bengaluru, IN

If your backend is slow, your APIs keep breaking under load, or your AI features feel bolted on instead of built in, that's usually where I come in. I build Django and FastAPI backends on AWS for SaaS and AI products that need to stay fast under real user load, not just pass a demo. Most of my work is either fixing slow APIs and deployment bottlenecks or shipping new backend features (REST APIs, async jobs, LLM/RAG integrations) for teams that outgrew their first architecture. I'm a senior Python backend developer based in India, with 8+ years, top-rated, and 100% job success. What I work on day to day: ★ REST APIs and backend services (Django/DRF, FastAPI, Flask when it fits) ★ Cloud deployments on AWS: EC2, S3, RDS, Lambda, Docker, CI/CD pipelines ★ Async and background jobs: Celery, Redis, RabbitMQ for queues, reports, notifications ★ AI and LLM integration: RAG workflows, chatbots, automation pipelines (OpenAI, Gemini, Grok APIs; not just prompt wrappers) ★ Backend refactoring: slow queries, messy codebases, deployments that break every release ★ Microservices where it makes sense: not microservices for the sake of it A few projects I've led or built recently: Astrosure.ai: AI-powered astrology platform from scratch. I led the backend: Django and FastAPI services, birth chart processing, dosha/dasha rules, and the Agastyaa chatbot with LLM integration. Deployed on AWS with Docker and CI/CD on EKS. HerKey (herkey .com): India's largest career platform for women. Built scalable APIs for job matching, session booking, and notifications. Django REST Framework, MongoDB, Elasticsearch, Kubernetes on AWS. We cut incident triage time by around 40% after improving monitoring and deployment flow. Riyaah (riyaah.sa): Beauty e-commerce platform in Saudi Arabia. Backend for product catalog, orders, and payment flows. Django, PostgreSQL, AWS. Before Upwork, I worked with Capgemini (banking/ANZ), Flooid, Keuro Digital, and Bricksteel, mostly backend, team lead, and client-facing delivery. I also run Sparkscribe Technologies for larger or long-term engagements where you need more than one developer. On Upwork you work with me directly. For larger scopes, I may involve senior developers from my company under my technical lead, only with your approval and with me owning architecture and delivery. How I usually work with clients: I keep communication straightforward, regular updates, and clear ETAs, with no disappearing mid-sprint. I've managed small teams (up to 10 devs), done code reviews, and set up CI/CD and monitoring so releases don't turn into firefights. Most of my Upwork clients mention responsiveness and clean delivery in their reviews. If you need someone who can own the backend end-to-end architecture, APIs, cloud, AI integration, and production stability, send me a message with a short description of what you're building. I'll reply with honest thoughts on scope and how I'd approach it.

Need a reliable backend — or want to add AI/LLM features to your product? You're in the right place. I'm a Backend Developer with 9+ years of experience building scalable web applications, REST APIs, and automation systems using Python, Django, and FastAPI. Over the last few years, I've specialized in integrating Generative AI and LLMs into real products — not demos, but production-ready features your users can actually rely on. Here's how I can help you: 🔹 Backend & API Development — Robust REST APIs and backend services with Django & FastAPI, built to scale. 🔹 AI & LLM Integration — GPT/LLM-powered features like text generation, chatbots, and intelligent automation using LLM APIs, RAG, and tools like n8n. 🔹 Automation & Web Scraping — Custom Python scripts that automate repetitive workflows and save you hours. 🔹 Database Design — Optimized PostgreSQL & MySQL structures for fast, reliable data handling. 🔹 Cloud Deployment — Deploying and maintaining applications on AWS. A bit about my background: I currently work as an Associate Consultant Developer at GlobalLogic, building Python automation and AI-powered backend workflows. I've also led development teams and delivered healthcare, e-commerce, and SaaS applications for clients across multiple industries. What you can expect working with me: ✅ Clean, well-tested, maintainable code (Pytest, debugging, tuning) ✅ Clear communication and regular updates ✅ Deadlines I actually meet If you want a developer who understands both solid backend engineering AND modern AI, let's talk. Send me your project details and I'll reply with exactly how I'd approach it.

I build reliable Python systems for automation, API integrations, and data-driven trading workflows designed to run cleanly under real-world constraints, not just in notebooks. What I specialize in: ⦁ Automation & Scripting – Production-grade Python automation for reports, workflows, and scheduled jobs. ⦁ API Integration – Reliable REST & WebSocket integrations for brokers, exchanges, and data providers. ⦁ Data Engineering – Ingestion, transformation, and storage pipelines using Pandas, NumPy, and SQL. ⦁ Trading Systems & Backtesting – Strategy logic, signal generation, backtests, paper trading, and PnL/risk analysis. ⦁ Internal Tools – Lightweight FastAPI or Streamlit tools for monitoring, controls, and diagnostics. How I work: ⦁ I focus on clear scope, clean execution, and practical trade-offs. Most projects start with a short discussion, paid review, or small prototype so we can validate assumptions before scaling. ⦁ I’m comfortable working on both focused tasks (debugging, optimization, API integration) and structured fixed-price projects, depending on what makes sense technically and commercially. ⦁ My goal is to deliver solutions that are reliable, understandable, and easy to extend — not quick hacks that break later.

Once an Astrophysics researcher. Now I help startups to enterprises to build scalable AI systems that outlive the hype cycle. Six months ago, everyone wanted RAG. Then Agentic RAG. Then Knowledge Graphs. Now it's Context Engineering a new "breakthrough" every other week. Your hardest problem isn't picking the right AI model. It's that by the time you've built a team around it, the ground has shifted. And you're responsible for a business outcome, not for building an AI team. Every month spent hiring, architecting, and second-guessing eats the one thing you can't buy back: your time. That's where we come in. I'm Vivekjyoti Bhowmik, 3× AI founder: → TOINGG — AI Communication OS + AI-first CRM. $700M+ in qualified sales pipeline generated.  → PG-AGI — 40-member AI engineering studio: design, engineering, research, deployment. (Why you're reading this.)  → BigAIRLab — our research lab, stress-testing every AI breakthrough so you don't bet your product on hype. Few selected Wins: → 80+ production AI systems across healthcare, fintech, LegalTech, EdTech, SaaS, logistics  → 70%+ still live in production, used by real customers  → Branify: 50,000+ users in month one, $2.5M revenue in two months  → 2M+ financial signals daily through AIMI Brain  → 45% higher open rates with Email Love (40K+ users) · 45% fewer returns with Mirror Me What we build: AI agents & multi-agent systems · RAG & vector-database pipelines for enterprises · production LLM pipelines (Claude API, OpenAI, Gemini) , observability & cost control · AI voice agents · AI automation & workflow automation for outcomes · full-stack AI SaaS AI APP development.... anything in Tech world.. Our deepest niche: speech-to-speech and small language models. We fine-tune full-duplex speech-to-speech models (Moshi architecture) to telephony-grade for enterprise voice, and deploy SLMs for on-device and edge AI. Open-source releases coming soon. We're a fit if: your budget starts at $5,000 quality over quantity. You have 5+ years in business and $500K+ in revenue. How we work: No hourly billing you pay for outcomes, not time. Before we start, we map your project with our Power Law Roadmap: the 20% of milestones that drive 80% of your outcome. Every milestone has a defined outcome in writing you don't approve it, and don't pay it, until it's in your hands. And as a founder, I'll tell you what vendors won't: half your list shouldn't be built yet. Two ways to start: Message me one paragraph on what you're building. You'll get a straight answer: what to build, what to skip, whether we're the right team. Book a consultation I'll personally run your idea through the Power Law Roadmap. You leave with a milestone map you can execute with us or anyone else. Worst case: a milestone map that saves you six months of building the wrong thing. Best case: the last AI partner you'll need. Talk soon, Vivekjyoti Key words to find me: AI Agents · Multi-Agent Systems · RAG · Vector Databases · LLM Integration · Claude API · OpenAI API · Speech-to-Speech · Full-Duplex Voice AI · Voice Agents · SLM · On-Device AI · Edge AI · Fine-Tuning (LoRA) · Quantization · Workflow Automation · Context Engineering · LLM Observability · Multi-Tenant SaaS · ERP ·CRM LegalTech · FinTech · Healthcare · EdTech · Logistics

I'm a Senior Data Engineer with 8+ years of strong technical expertise in building reliable and scalable data infrastructure, from data ingestion to transformation to warehousing, streaming, and data analytics, specializing in dbt, Snowflake, Airflow, Databricks (and more) across AWS, Azure, and GCP, with robust ELT and ETL pipelines. If your data pipelines are brittle, your data warehouse is slow, or your data was never built to scale, that is exactly what I fix, with fault tolerance, observability, and audit-ready quality engineered in from day one. I cover the full data engineering lifecycle: batch and real-time data pipelines, Modern Data Stack builds, lakehouse architecture, cloud and warehouse data migration, governance, and the data foundations that feed modern systems. 🎯 Core Expertise: ✅ Data Pipelines & Orchestration: End-to-end batch and real-time pipelines with Apache Airflow, Dagster, Prefect, AWS Step Functions, and Azure Data Factory. Idempotent, schema-drift tolerant, and monitored so failures surface before they reach your stakeholders. ✅ Cloud Warehousing & Lakehouse: Snowflake, BigQuery, Amazon Redshift, Databricks, and Microsoft Fabric, with Delta Lake and Apache Iceberg lakehouse foundations governed through the Glue Data Catalog and Lake Formation, with Athena and Redshift Spectrum for serverless queries, Medallion Architecture, partitioning, and performance tuning. ✅ Data Transformation & Modeling: dbt (Core and Cloud), SQLMesh, Spark and PySpark on EMR and AWS Glue, Star Schema and dimensional modeling, analytics engineering best practices, full test coverage, and CI/CD for data models. ✅ Streaming & Real-Time Analytics: Distributed streaming with Apache Kafka, Flink, Spark Structured Streaming, Kinesis, and Pub/Sub, including exactly-once semantics, dead-letter queues, CDC, and end-to-end latency guarantees. ✅ Data Ingestion & Integration: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, and custom CDC pipelines for near-real-time sync across structured, semi-structured, and unstructured sources. ✅ Data Quality, Governance & Observability: Automated data quality frameworks, SLA monitoring, auditable lineage, data catalog and metadata management, and observability that catches bad data early. ✅ Cloud Migration & Modernization: Zero-downtime migration handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime, replacing brittle ETL and ELT with a clean Modern Data Stack. ✅ AI-Ready Data Infrastructure: Pipelines engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ------------------------------------------------------ ⚙️Tech Stack: ⚡ Warehouses & Lakehouse: Snowflake | BigQuery | Redshift | Databricks | Microsoft Fabric | Athena | Delta Lake | Iceberg ⚡ Transformation: dbt | SQLMesh | Spark | PySpark | AWS Glue | EMR | Star Schema | Medallion Architecture ⚡ Orchestration: Airflow (GCP Cloud Composer and AWS MWAA) | Dagster | Prefect | Azure Data Factory | Step Functions ⚡ Streaming: Kafka | Flink | Kinesis | Pub/Sub | Spark Structured Streaming | ClickHouse ⚡ Ingestion: Fivetran | Airbyte | Matillion | Stitch | Hevo | Meltano | CDC ⚡ Governance & Catalog: Glue Data Catalog | Lake Formation | Unity Catalog | Microsoft Purview | Dataplex ⚡ Cloud: AWS | GCP | Azure ⚡ Languages: Python | SQL (Snowflake, BigQuery, T-SQL, PL/pgSQL) | FastAPI ⚡ Databases: PostgreSQL | MySQL | SQL Server | DynamoDB | MongoDB ⚡ BI & Reporting: Looker | Tableau | Power BI | GA4 | Metabase | Superset | Streamlit | Grafana ------------------------------------------------------ ⭐ What Clients Say: 🏅 "Adarsh rebuilt our analytics pipeline on Snowflake, Airflow, and dbt, giving us reliable, version-ready data. Reporting accuracy improved overnight, and we can finally trust the numbers." – Anita, Head of Product, FinTech SaaS 🏅 "He designed a zero-downtime migration to a modern data warehouse that cut query latency by more than half while keeping our SLAs intact." – Daniel, VP of Data, AdTech Firm 🏅 "Clean architecture, solid dbt models, and Airflow pipelines running without issues for months. He brought a level of engineering discipline we hadn't seen from a data consultant before." – Mark, Director of Data Engineering, E-commerce Startup 🏅 "We came to him with a Spark pipeline costing us a fortune and delivering stale data. He restructured the workflow logic and cut processing time by 70%." – Leo, Head of Analytics, HealthTech SaaS ------------------------------------------------------ 🏆 TOP RATED PLUS | EXPERT-VETTED | Top 1% on Upwork | 8+ Years Experience | 100% Job Success 🚀 Ready to build a scalable, production-ready data infrastructure to turn your raw data into reliable, actionable business insights? Click the 'Invite to Job' button on the top right, and let's discuss your data pipeline!

Building an AI agent or RAG demo is easy. Making it reliable, secure, observable, and cost-predictable in production is where most projects fail. I design, build, and rescue production AI systems on AWS for SaaS and enterprise teams. Expert-Vetted (Top 1% on Upwork) | 100% Job Success | $80K+ earned 2,700+ hours | 41 jobs | 3x AWS Professional certified (Generative AI, Solutions Architect, DevOps) | 4x AWS Community Builder | 9+ years CLIENTS BRING ME IN WHEN - An AI agent works in a demo but breaks with real users, tools, permissions, retries, or live business data - A RAG system retrieves the wrong context or produces weak citations - AI inference and cloud spend swing unpredictably month to month - A proof of concept needs production architecture, security, and a safe deploy and rollback path - Claude, OpenAI, or Amazon Bedrock must connect to real workflows - An AWS platform has reliability, scaling, security, or cost problems WHAT I BUILD AI Agents and Agentic Workflows - Multi-agent systems with LangChain, LangGraph, tool calling, and MCP - State management, tracing, approvals, and human-in-the-loop escalation - Deterministic fallback paths for when the model is the wrong tool Enterprise RAG and Knowledge Systems - Hybrid retrieval (dense embeddings + BM25), reranking, and citations - Vector stores: OpenSearch, Pinecone, Qdrant, pgvector, FAISS - Tenant isolation and access control for multi-tenant data LLM Integration and Evaluation - Claude, OpenAI, Amazon Bedrock, and open-source models - Prompt engineering, guardrails, evals, and regression tracing - Cost and latency optimization for production inference AWS Platform and Serverless - Event-driven systems: Lambda, API Gateway, DynamoDB, SQS, EventBridge, Step Functions - ECS, EKS, RDS, OpenSearch, ElastiCache - Terraform, AWS CDK, CloudFormation, SAM - IAM least-privilege, observability, incident recovery, cost optimization TECHNICAL STACK Python, Node.js, TypeScript | LangChain, LangGraph, MCP | Claude, OpenAI, Amazon Bedrock | OpenSearch, Pinecone, Qdrant, pgvector | AWS Lambda, DynamoDB, Step Functions, EventBridge | Terraform, CDK, Docker, Kubernetes SELECTED PRODUCTION OUTCOMES - 43% reduction in monthly Lambda cost - 55% improvement in processing time - Event-driven ordering platform handling millions of transactions per month with strict uptime and recovery requirements - Production integrations across Claude, OpenAI, and Amazon Bedrock I AM A GOOD FIT IF - You want a system that survives real users, not another demo - You have real workflows, data, or a running product to build on - You want senior ownership and clear architectural decisions I AM NOT THE FIT IF - You are exploring AI with no use case yet - Price matters more than reliability and outcomes HOW I WORK I do not start with a framework. I start with your workflow, your data, your permission model, your failure modes, and your operating cost. That includes telling you when not to build something. I have advised clients against AI agents where a deterministic workflow was safer, against RAG where a direct query was enough, and against managed services that add cost or lock-in without earning it. Guardrails, evals, and tenant isolation reduce risk and improve reliability; they do not eliminate hallucinations, and I will not claim they do. Send me your architecture, repo context, or the production issue you are stuck on. I will identify the highest-risk path to production and the smallest safe step to fix it.