Hire the Best Data Scientists in Hyderabad, IN

I have 6+ years experience as an ML/NLP Engineer. I worked with Micro1 where we developed a smart networking app which was aimed at encouraging and reconnecting old friends to meet where an AI bot came up with plans matching friends and planning the outing based on preferences. I managed the entire ML framework for this app combining Gen AI tools with ML algorithms for matching. I have experience with finetuning gen AI tools, RAG, using copilot to enhance code. I worked with a company to develop a virtual assistant using gpt 3.5 and pincone with the help of langchain using RAG I have worked on various data extraction and classification models. I work on Python and have worked with various libraries, Spacy, scikit learn, pandas, numpy being a few. I have been working remotely for the last 5 years so I am familiar with working on projects on remote servers and version control through git. I have a Masters Degree in Computer Science from the University of Southern California.

A pharmacist with over 3 1/2 of research and clinical application experience. I am trained and licensed to provide expertise in pharmacy, pharmaceutical sciences, pharmacovigilance, pharmacology, clinical outcomes, biology, biochemistry, and genetics. I am detail oriented, adaptable, dependable and most importantly passionate about patient care and the profession of pharmacy. I am also technologically savvy, fluent in PC applications and have the ability to utilize professional clinical tools and top databases (PubMed, Clinical Pharmacology, Natural Medicines, etc.). Invested in carrying out research and in-depth analysis to provide evidence and insights that feeds healthcare decisions, pharma business output and regulatory decisions. Particularly skilled in secondary research on medical literature databases, clinical trial research (Analyzing clinical trials from journals, meeting abstracts, presentations, clinicaltrials.gov and providing valuable information to bio-pharmaceutical companies.), systematic literature reviews, medical writing, pharmacovigilance reports and project management. Expert in tracking regulatory filings and analyze the regulatory information using data sources like SEC files, annual reports, FDA, EMEA & other regulatory websites, abstracts, journals, conferences & events. Performed all data management tasks as appropriate to include, but not to be limited to data review and query management to ensure that quality standards (both internal and client) are achieved.  Performed reconciliation of the clinical database against safety data, laboratory data, ECG, PK and other third-party data as appropriate  Reviewed clinical trial data in accordance with Data Management Plans, Edit Check Specifications and/or Data Review Guidelines and raise queries to resolve erroneous, missing, incomplete data  User Acceptance Testing (UAT)  Participated in Internal Core team and Client meetings and supports the Lead Data Manager with risk management on allocated projects. . I pay adequate attention to all my projects so deliverables are developed excellently and completed in a timely manner thanks to my years of research, medical writing and project management. I am very efficient, organized and result oriented. Feel free to contact me if you require my services. I look forward to working with you.

I'm a data science enthusiast with a strong foundation in theory and real-world experience. I'am working as an Associate Software Engineer at Kore Company. Graduated in 2023 with a specialization in data science, I'm eager to leverage my skills in: Data Wrangling & Cleaning: Python (Pandas, NumPy), SQL, Data Pipelines Exploratory Data Analysis & Visualization: Seaborn, Matplotlib, Tableau Machine Learning: Supervised & Unsupervised Learning algorithms (Regression, Classification, Clustering) Model Development & Evaluation: Scikit-learn

Need AI Transformation for your business or enterprise ? Have a pile of documents, or a process eating your team's week, that you know AI should handle, but every attempt so far has been a demo nobody trusts? I am your AI guy ! Hi, I'm Bharadwaj. Over the past years I've taken enterprise AI from idea to production for a Fortune 500 manufacturer, a healthcare product that has handled 17,000 calls, and a SOC 2 Type II fintech. I'm certified in RAG quality by Maven, and I hold Top Rated Plus with 100% Job Success. Anyone can build an AI system now, so can I. But AI projects don't die on the software/code layer. They die in the gaps between your vision and your engineer's lack of understanding: the silent week, the scope that quietly drifts, the "almost done" that never ships, the dev who goes dark the moment it gets hard. This is how I'm different. I run your build like a founder who owns the outcome. Replies in hours. A working slice you can test every week. Honest answers, including "that is a bad idea, and here is why," before you spend a dollar on it. You always know where your build stands and what comes next. The hard engineering still happens underneath: every answer cites its source, evaluations run from day one, and I ship under HIPAA or SOC 2 with a signed BAA when your data is regulated. But on a real build, clear communication is what actually ships it. What clients say: "He is one gun AI ML developer. Our new permanent for all AI ML and Python work in future." - Upwork client (5.0) Last updated: 1st August 2026 → Results: - Certified in RAG quality (Maven, Systematically Improving RAG Applications) - Shipped enterprise AI into production under HIPAA and SOC 2 Type II - Cited, multilingual RAG for a Fortune 500 manufacturer - A HIPAA voice system that has handled 17,000 calls - A back-office automation that cut a 4-person, roughly $200,000-a-year task by 80 to 90 percent - 4,406 hours delivered, 5.0 across 19 reviews, 1,600-hour contracts held with 100% client satisfaction. → How I help you: - Validate your AI architecture before you spend a dollar on the wrong stack - Build cited RAG your team actually trusts, over your own documents - Engineer out hallucinations with real evaluation, not vibes - Handle PHI and regulated data under HIPAA or SOC 2, and sign a BAA - Let non-technical staff query your database in plain English with text-to-SQL - Automate the manual, repetitive work quietly eating your team's hours Here is everything I have experience in: → RAG and Retrieval Enterprise RAG, Retrieval Augmented Generation, GraphRAG, multimodal RAG, chunking and embedding strategy, reranking, hybrid search, source citation, hallucination control, permission-aware retrieval, LlamaIndex, LangChain, PropertyGraphIndex, Neo4j → AI Agents and Orchestration LangGraph, CrewAI, AutoGen, MCP servers, multi-agent systems, agentic workflows, tool calling, long-term memory, workflow orchestration. → Voice AI LiveKit, Twilio, Whisper, Eleven Labs, WebRTC, inbound receptionist, outbound calling, telephony, real-time transcription, structured extraction from audio, call analytics → LLMs and Fine-Tuning GPT, Claude, Llama, Gemma, DeepSeek, Mistral, prompt engineering, structured outputs, function calling, reinforcement fine-tuning, LLM fine-tuning, Hugging Face → Vector Databases Pinecone, Qdrant, Chroma, Weaviate, pgvector, Azure AI Search → Evaluation and Observability RAGAS, LangSmith, Langfuse, golden-set evaluation, regression testing, LLM evaluation, prompt testing → Document AI and Extraction PDF, Word, Excel, and PowerPoint ingestion, OCR, table detection, structured extraction, text-to-SQL, invoice and report processing, deduplication → Microsoft and Enterprise Stack Azure, Azure OpenAI, MS Graph, Teams, Outlook, OneDrive, SharePoint, Office 365, single sign-on, Active Directory → Compliance and Security HIPAA, SOC 2 Type II, BAA, RBAC, PHI handling, audit logging, prompt-injection defense, data governance, GDPR-aligned architecture → Backend and Infrastructure Python, FastAPI, Django, Flask, Node.js, Docker, Kubernetes, AWS, Azure, GCP, CI/CD → Data PostgreSQL, MongoDB, Redis, Elasticsearch, data pipelines, ETL → Integrations Salesforce, HubSpot, QuickBooks, Plaid, Stripe, Slack, REST, GraphQL, webhooks → Industries I've built for: Manufacturing, Healthcare, Fintech, Insurance, Logistics and Shipping, Corporate Housing, Legal, Retail and Luxury, SaaS If you're serious about getting AI into production, let's talk. One call can save you a six-month science project and tens of thousands on the wrong build. Tell me the documents your team keeps re-reading or the task eating their week, and I'll send back a short Loom within 24 hours with the architecture and what it saves. Bharadwaj S. | Enterprise AI Engineer

Lead Data Scientist & Agentic AI Engineer — Toptal-vetted, with a decade of shipping production ML and AI systems across retail, fintech, e-commerce, edtech, telecom, and marketplaces. I build LangGraph agents, forecasting and NLP models, and the FastAPI/MCP infrastructure that turns them into real products — not notebooks that die in dev. ──────────────────────────────────────────── WHAT I DO BEST 🤖 Agentic AI & LLM Engineering - LangGraph and LangChain agents — multi-agent, stateful, tool-using - MCP (Model Context Protocol) server design and integration - RAG systems with proper evals, not vibes - FastAPI-based AI services with observability built in 📊 Applied Machine Learning - Time series forecasting — demand, sales, financial - NLP — classification, extraction, summarization - Churn prediction, customer segmentation, credit risk modeling - Large-scale binary classification with PySpark + XGBoost, including value-weighted threshold optimization (because AUC isn't a business metric) 🏗️ Production ML Engineering - PySpark pipelines for training and inference at scale - Model serving via FastAPI — versioning, monitoring, the boring stuff that turns models into revenue ──────────────────────────────────────────── HOW I WORK → 30-min scoping call (free) before any engagement — I won't take projects I can't ship well → Clean, runnable code — no half-baked snippets or "should work" handoffs → Direct on tradeoffs and timelines → I document as I go so you're never left with a black box → Async-first, but I overlap hours with US/EU/AU teams as needed ──────────────────────────────────────────── TYPICAL ENGAGEMENTS - Build a LangGraph agent system (4–8 weeks) - Forecasting model + production deployment (3–6 weeks) - NLP pipeline for document extraction or classification (2–5 weeks) - ML system audit + rebuild (2–4 weeks) - Fractional ML lead for AI-first startups (ongoing, part-time) ──────────────────────────────────────────── TECH STACK Python · PyTorch · Scikit-learn · XGBoost · PySpark · LangChain · LangGraph · FastAPI · MCP · AWS · GCP · Databricks · Snowflake · Docker · Git ──────────────────────────────────────────── If you're building an AI/ML system that needs to actually work in production, let's talk. I'll tell you honestly whether it's a fit.

As a highly skilled and experienced Deep Learning Engineer, my track record speaks volumes. I have successfully developed and deployed advanced deep learning models to address intricate business challenges. My expertise encompasses a profound understanding of various deep learning algorithms, coupled with proficiency in programming languages such as Python, R, and SQL. Additionally, I am adept in utilizing cutting-edge tools like TensorFlow, Keras, and scikit-learn to their fullest potential. Allow me to highlight some of my notable achievements. For instance, I spearheaded the development of a predictive model for customer churn, resulting in a remarkable 20% reduction in churn rates for a prominent telecom company. Furthermore, I led a proficient team in constructing a computer vision model for product defect detection, which substantially augmented efficiency by 30%. My academic background is equally robust, holding a Master's degree in Computer Science with a specialization in Artificial Intelligence. Moreover, I have contributed to the field through the publication of several research papers, further solidifying my expertise. In essence, I am a results-driven, innovative, and dedicated Deep Learning Engineer, equipped with the skills and knowledge to collaborate seamlessly with cross-functional teams in delivering superior, forward-thinking solutions.