Subham patnaik

Data Scientist

Hyderabad, Telangana, India2 yrs 4 mos experience

AI EnabledHighly Stable

Key Highlights

Expert in building production-grade AI systems.
Certified Azure and AWS Data Engineer.
Proven track record in deploying scalable AI applications.

Stackforce AI infers this person is a SaaS-focused AI Engineer with expertise in scalable application development.

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Skills

Core Skills

Ai / Genai EngineerData Science

Other Skills

SQLPython (Programming Language)FastAPILarge Language Models (LLM)Retrieval-Augmented Generation (RAG)Machine LearningCI/CD for AI SystemsAPI & Microservices DesignDockerKubernetesGitHub ActionsLangGraphLangChainNatural Language Processing (NLP)Prompt Engineering & Guardrails

About

Application Developer building production-grade AI systems with Python and GenAI. I design and ship scalable applications using LLMs, RAG, vector search, and API-driven AI services, optimized for latency, cost, and reliability. Certified Azure Data Scientist Associate and AWS Data Engineer Associate, with hands-on experience deploying AI pipelines and cloud-native architectures that move cleanly from prototype to production. I take end-to-end ownership: translating ambiguous business problems into system design, implementing and deploying solutions, and measuring impact with clear metrics. I prioritize correctness, scalability, and systems that hold up under real load.

Experience

2 yrs 4 mos

Total Experience

2 yrs 4 mos

Average Tenure

2 yrs 4 mos

Current Experience

Ibm

Application Developer

Feb 2024 – Present · 2 yrs 4 mos

Architected and deployed production-grade RAG pipelines using embeddings, vector search, and LLM-based retrieval, enabling scalable enterprise knowledge access and reducing manual query effort across teams.
Designed document ingestion, chunking, and indexing systems optimized for semantic search, powering high-precision contextual Q&A and knowledge discovery at scale.
Built low-latency AI microservices using FastAPI, supporting chatbot and application integrations with reliable API contracts and monitoring-ready deployments.
Reduced LLM hallucinations by ~40% through prompt engineering, retrieval grounding, and response validation strategies, significantly improving answer accuracy and user trust.
Implemented automated CI/CD pipelines for AI services using Docker, Kubernetes, and GitHub Actions, improving deployment reliability and accelerating release cycles.
Drove GenAI adoption by partnering with product and business stakeholders to define use cases, translate requirements into system design, and measure impact against clear KPIs.

SQLPython (Programming Language)FastAPILarge Language Models (LLM)Retrieval-Augmented Generation (RAG)Machine Learning+3