APOORV OMAR

Software Engineer

Bengaluru, Karnataka, India6 yrs 3 mos experience
AI EnabledHighly Stable

Key Highlights

  • Expert in architecting GenAI systems for enterprise applications.
  • Led high-impact projects in AI and machine learning.
  • Passionate about mentoring and transforming business challenges.
Stackforce AI infers this person is a SaaS and Fintech expert specializing in AI systems architecture and machine learning.

Contact

apoorvomar19@gmail.comLinkedIn

Skills

Core Skills

Genai Systems EngineeringAi ArchitectureAi-powered Paas DevelopmentConversational AiLlm Model TrainingRag DesignMachine LearningData Science

Other Skills

AI PromptingAI systemsAPI developmentAgentsAlgorithmsApplied Machine LearningArtificial Intelligence (AI)Attention MechanismsBERTCNNClassificationComputer ScienceComputer VisionConvolutional Neural Networks (CNN)Cricket

About

Iโ€™m a Staff GenAI Engineer passionate about building reasoning-driven, agentic AI systems that bridge the gap between research and real-world impact. Over the past 5 years, Iโ€™ve evolved from a traditional machine-learning engineer into a GenAI systems architect, leading initiatives that combine LangGraph, MCP, RAG pipelines, LLMs, and knowledge graphs to deliver scalable intelligence for enterprise applications. My journey began with hands-on experimentation in classical MLโ€”feature engineering, predictive modeling, and deployment pipelines. As the AI landscape shifted, I transitioned toward large-language-model-based architectures, designing and implementing end-to-end GenAI frameworks that integrate retrieval, reasoning, and orchestration. At 4๐‚๐‘๐ข๐ฌ๐ค.๐š๐ข, I led multiple high-impact projects: โ—พ AI-powered PaaS development: engineered a platform that unified backend, frontend, and PM workflowsโ€”enhancing productivity and standardizing experimentation. โ—พ Conversational AI (ARIA Co-pilot): designed a domain-specific RAG system for financial compliance, scaling to 10 K+ users with 70 % accuracy verified by clients. โ—พ LLM fine-tuning & optimization: delivered low-latency inference pipelines using LoRA, PEFT, and QLoRA, improving efficiency by 200 %. โ—พ Prompt engineering & evaluation: built structured evaluation frameworks to test reasoning consistency, factual accuracy, and robustness across agents. Today, at ๐“๐ฎ๐ซ๐ข๐ง๐ , I focus on GenAI systems engineering at scaleโ€”designing reliable orchestration layers, evaluation workflows, and adaptive reasoning agents that integrate with business logic, event streams, and cloud infrastructure. My goal is to make intelligent agents production-ready, cost-efficient, and interpretable. Beyond code, I enjoy mentoring teams, defining AI roadmaps, and transforming ambiguous business problems into deployable solutions. I believe the future of AI lies in systems that can reason, collaborate, and evolve autonomously, and Iโ€™m dedicated to building the foundations that make this possible. ๐‚๐จ๐ซ๐ž ๐„๐ฑ๐ฉ๐ž๐ซ๐ญ๐ข๐ฌ๐ž: LangGraph | MCP | LLMs | RAG | Agentic AI | LLM Evaluation | Knowledge Graphs | Vector Databases | Deep Learning | NLP | MLOps | LoRA | Prompt Optimization | Python | Pytorch | Docker | GCP | Azure | FastAPI | LangChain | Transformer Models ๐–๐ก๐š๐ญ ๐๐ซ๐ข๐ฏ๐ž๐ฌ ๐ฆ๐ž: turning cutting-edge LLM research into real-world systems that scaleโ€”whether itโ€™s optimizing inference, enhancing retrieval, or enabling intelligent workflows through autonomous reasoning.

Experience

6 yrs 3 mos
Total Experience
2 yrs 8 mos
Average Tenure
11 mos
Current Experience

Turing

Staff GenAI Engineer

Jul 2025 โ€“ Present ยท 11 mos ยท Remote

  • Leading the design and development of reasoning and agentic AI systems using LangGraph, MCP, and LLMs for enterprise-scale applications. My focus is on building modular GenAI architectures that enable reliable orchestration, retrieval-augmented generation (RAG), and intelligent tool usage across diverse domains.
  • Key Contributions:
  • โ—พ Architecting a multi-agent platform for autonomous task orchestration and contextual decision-making.
  • โ—พ Implementing LLM evaluation pipelines for reasoning accuracy, latency, and factual consistency.
  • โ—พ Integrating MCP-based connectors (Outlook, Snowflake, Box, etc.) to enable end-to-end workflow automation.
  • Driving best practices in prompt design, caching, and system scalability for production-grade AI systems.
LangGraphMCPLLMsAI systemsworkflow automationGenAI systems engineering+1

4crisk.ai

3 roles

Lead Data Scientist

Aug 2024 โ€“ Jun 2025 ยท 10 mos ยท On-site

  • 1. AI-Powered PaaS Development: Built a Platform-as-a-Service (PaaS) to enhance efficiency and productivity for backend and frontend teams while assisting PMs in conducting PoCs.
  • 2. Conversational AI System: Led the design and development oversight of ARIA Co-pilot (RAG) in the finance sector, improving functionality and performance.
  • 3. Scalability & Optimization: Spearheaded efforts to scale ARIA Co-pilot (RAG) for 10k+ users by optimizing infrastructure and algorithms for enhanced reliability and responsiveness.
  • System Accuracy Improvement: Enhanced data parsing, query extraction, retrieval, and ranking, achieving over 70% accuracy as verified by clients.
  • 4. LLM Development & Fine-Tuning: Developed and deployed an open-source LLM for internal use, including fine-tuning with LoRA, PEFT, and QLoRA techniques.
  • 5. High-Performance Inference Pipeline: Designed and implemented an ML inference pipeline, boosting inference speed by 200% while optimizing resource utilization and scalability.
  • 6. Prompt Engineering: Optimized prompts(chain of thought) to align with project requirements, ensuring better AI performance.
PaaSConversational AIRAGLLM fine-tuningPrompt engineeringAI-Powered PaaS Development

Senior Data Scientist

Promoted

Dec 2022 โ€“ Feb 2025 ยท 2 yrs 2 mos ยท On-site

  • 1. Responsible for training and deployment of LLM model.
  • 2. Design a end-to-end Retrieval augmented generation(RAG) flow for the compliance domain.
  • 3. Responsible for feature selection for ML models related task.
  • 4. Design data strategies for ML models.
LLM trainingRAGfeature selectionLLM model trainingRAG design

Data Scientist

Jun 2022 โ€“ Dec 2022 ยท 6 mos ยท On-site

  • 1. Built and deployed some task specific ml models like key-word extraction, summarization and classification model etc.
  • 2. Write an api to generate the compliance map (Regulation vs control mapping).
  • 3. Build some machine learning based utility to automate task which enhance the productivity by 20%.
ML modelsAPI developmentautomationMachine Learning

Simplifyvms

2 roles

Data Scientist

Jul 2020 โ€“ Jun 2022 ยท 1 yr 11 mos

  • 1. Work on Resume Parser and Job parser.
  • 2. Implement Transformer based NER with entity relation.
  • 3. Build some Machine learning basd utilities and develop different approaches for different seanario which inhance the accuracy by 10%.
  • 4. Structuring and parallelization of code base which reduce the time by 15%.
  • 5. Modified libraries: pymupdf and datepicker based on requirement.
Resume ParserNERMachine Learning utilitiesData Science

Machine Learning Intern

Jan 2020 โ€“ Jun 2020 ยท 5 mos

  • 1. Work on Resume Parser.
  • 2. Increase the accuracy by 10% by providing another approach.
  • 3. Implement NER Models.
Resume ParserNERMachine Learning

Signy advanced technologies

3 roles

Machine Learning Intern

Jun 2019 โ€“ Jul 2019 ยท 1 mo

  • 1. Work on Digital Onboarding System.
  • 2. Implement Face verification with liveness.
  • 3. It reduces 50% time in user onboarding system and also provides the user's interactivity.
Digital Onboarding SystemFace verificationMachine Learning

machine learning intern

Mar 2019 โ€“ May 2019 ยท 2 mos

  • 1. Work on Face Verification System.
  • 2. Increased model accuracy by 10% on the desired conditions.
Face Verification SystemMachine Learning

Machine laerning intern

Dec 2018 โ€“ Feb 2019 ยท 2 mos

  • 1. Implement CNN(convolution neural network) to differentiate speakers on the basis of their voice.
  • 2. It enhances user security.
CNNMachine Learning

Education

KRISHNA INSTITUTE OF ENGINEERING AND TECHNOLOGY, GHAZIABAD

B.Tech โ€” computer science and engineering

Jan 2016 โ€“ Jan 2020

Stackforce found 100+ more professionals with Genai Systems Engineering & Ai Architecture

Explore similar profiles based on matching skills and experience

Mike Thacker-Cooke

Mike Thacker-Cooke

Director of Recruitment

at KDR Talent Solutions

Middlewich, United Kingdom13 yrs 11 mos exp
Data RecruitmentTechnology RecruitmentBusiness Intelligence RecruitmentData Management Recruitment
Deepak Gupta

Deepak Gupta

Talent Acquisition Group-Leadership Hiring (PAN India)

at EPAM Systems

Delhi, India16 yrs 3 mos exp
Talent AcquisitionLeadership Hiring
Mani Bhushan

Mani Bhushan

Engineering leader

at OrbitronAI.com

Bengaluru, India10 yrs 7 mos exp
System ArchitectureCross-functional Team LeadershipGeospatial DataGeospatial IntelligenceAmazon Web Services (AWS)
Rajendra Prasad

Rajendra Prasad

R&D Engineering Director

at Synopsys Inc

Mountain View, United States27 yrs 2 mos exp
ManagementMachine LearningLeadershipCustomer EngagementPeople Management
Shripad Deshpande

Shripad Deshpande

Head of R&D - Hardware Design and EDA

at Chipforge.ai

India27 yrs 2 mos exp
EDASoCVirtual PrototypingASIPRTL design
Mahammed Hussain

Mahammed Hussain

Asic Digital Design Staff Engineer, Mixed signal IP (Multi protocol Serdes PHY)

at Synopsys Inc

Bengaluru, India4 yrs exp
Digital system designPCIe protocolASIC designRTL development
Ramachandran C

Ramachandran C

Assistant Consultant

at TCS

Bengaluru, India11 yrs 5 mos exp
Technical WritingRequirements GatheringTechnical Communication
NithishSharavanan S K

NithishSharavanan S K

Software Development Engineer

at BlueSapling

Coimbatore, India2 yrs 2 mos exp
Software DevelopmentAPI Development