Raja Rajendran — CTO

§ A software engineering leader with extensive technical leadership, strong team building, people management and hands on skills, focused on building software products and platforms. Have a passionate interest in Software Product Engineering, Machine Learning, Deep Learning, Generative-AI and contemporary technologies. § Have a solid track record of shipping several software products ( for startups and enterprises) on time, with stringent scaling, HA and DR requirements, with innovation and quality. Have a proven history of building strong software engineering teams § Extensive experience working in large enterprises and startups, working with geo distributed teams and with various stakeholders including product management, customer support, customer advocacy (services), finance, HR and others § My broad areas of leadership and expertise include: software team building (hiring, mentoring, retaining), engineering ownership of critical customer accounts, Software Product Development, Deep Learning (Pytorch), Generative-AI, RAG and Agent based LLM applications, model fine tuning, MLOps/LLMOps, Machine Learning (scikit.learn), Applications architected on Public cloud (AWS, GCP), Microservices, Kubernetes, Scalable Persistence layers (NoSQL, Relational, Document Stores, others), Backend engineering, programming languages (Python, Java, Golang, Javascript) and their frameworks, networking and others. https://github.com/software-artisan https://github.com/concurrent-ai/concurrent https://medium.com/infinstor/logbert-log-file-anomaly-detection-using-bert-an-explainer-db20bfd2f91f

Stackforce AI infers this person is a SaaS and AI expert with a strong focus on MLOps and Generative AI.

Experience: 22 yrs 5 mos

Skills

Machine Learning
Generative Ai
Mlops
Cloud Computing
Microservices
Software Development

Career Highlights

Proven track record of shipping software products on time.
Expertise in building strong software engineering teams.
Passionate about contemporary technologies and AI.

Work Experience

Zillion Technologies, Inc.

Head of AI and Products (1 yr 9 mos)

InfinStor

Senior Director Of Engineering (3 yrs 9 mos)

Cisco

Senior Software Engineering Manager (7 yrs 3 mos)

Software Engineering Manager (2 yrs)

Technical Leader (7 yrs 8 mos)

Senior Engineer (3 yrs 5 mos)

Education

MBA at Santa Clara University

M.S at Northern Illinois University

at University of California, Santa Cruz

B.E at College of Engineering, Guindy

at Kshatriya Vidhyasala

Master of Science - MS at Northern Illinois University

Raja Rajendran

CTO

United States22 yrs 5 mos experience

Highly StableAI Enabled

Key Highlights

Proven track record of shipping software products on time.
Expertise in building strong software engineering teams.
Passionate about contemporary technologies and AI.

Stackforce AI infers this person is a SaaS and AI expert with a strong focus on MLOps and Generative AI.

Contact

Skills

Core Skills

Machine LearningGenerative AiMlopsCloud ComputingMicroservicesSoftware Development

Other Skills

Agentic AIDeep LearningRetrieval-Augmented Generation (RAG)Conversational AIPredictive AnalyticsLLMOpsPythonAWSKubernetesDockerLLMNLPCloud Native Broadband RouterCross Work SuiteEvolved Programmable Network Manager

About

Experience

22 yrs 5 mos

Total Experience

10 yrs 4 mos

Average Tenure

1 yr 9 mos

Current Experience

Zillion technologies, inc.

Head of AI and Products

Sep 2024 – Present · 1 yr 9 mos

Responsible for development, deployment and positioning of Agentic AI, Generative AI, Deep Learning, and Machine Learning systems across diverse customer verticals. Some key initiatives:
Autonomous Agentic AI: Engineered AI agents capable of multi-step reasoning and complex planning to achieve goal-oriented outcomes. Successfully deployed these agents to automate intricate business processes and research workflows.
Advanced RAG Pipelines: Developed high-precision Retrieval-Augmented Generation (RAG) systems specifically for the legal domain (automating court intake and legal research). Implemented sophisticated retrieval strategies, including hybrid search, semantic search, and graph search, coupled with query rewriting and result re-ranking.
Recommender Systems: Built and scaled product recommendation engines utilizing collaborative filtering, content-based filtering, and advanced two-stage neural filtering architectures.
Conversational AI: Built omni-channel virtual agents for contact centers, enabling seamless automated support across voice, text, web chat, and social media platforms.
Core ML Implementation: Deployed robust machine learning models for critical business functions, including predictive analytics, classification, anomaly detection, and time-series forecasting.

Agentic AIGenerative AIDeep LearningMachine LearningRetrieval-Augmented Generation (RAG)Conversational AI+1

Infinstor

Senior Director Of Engineering

Dec 2020 – Sep 2024 · 3 yrs 9 mos · On-site

§ An early employee (6th employee) of Infinstor, a startup with products in MLOps, LLMOps and Generative AI. Head of engineering and responsible for all software and data science teams.
§ Infinstor MLFlow (https://mlflowui.infinstor.com): an enterprise ready implementation of MLFlow (https://www.mlflow.org), a scalable, multi-tenant (SaaS), MLops/LLMops platform. Deployed at multiple enterprise customers, Infinstor MLflow offers Tracking, Model Registry, Model Management, Model Serving, Prompt Engineering and others. Platform supports Authentication (native and OAuth), Authorization, HA, DR and others. Built using Python, Java and AWS Lamdba, Sagemaker, Dynamodb, S3, Cloudformation, Cloudwatch, Cloudfront, API Gateway, Custom VPC, Event bridge and others.
§ Concurrent AI (https://github.com/concurrent-ai/concurrent; https://concurrent-ai.org/): an open source platform, developed by Infinstor, to run Deep learning and Machine learning pipelines with parallelization and with deep integration with Infinstor MLflow. The control plane runs in AWS and GCP and worker nodes run in Kubernetes (EKS, GKE, On prem kubernetes, AWS Fargate). Platform acquires and releases GPU and CPU resources aggressively for optimizing the cost of running pipelines. Built using Python, Kubernetes, Docker, AWS Lamdbas, Dynamodb, S3, Cloudformation, Cloudwatch, Cloudfront, API Gateway and others.
§ LLM Application for Salesforce Service Cloud: an LLM based RAG application for semantic search, summarization, auto responding to customer support cases in Salesforce service cloud. Built using Faiss vectorDB, SBERT/sentence transformers, LLMs (LLama-2, gpt-4), model finetuning, incremental data ingestion, llama-index/Langchain and SOTA techniques such as Chain of Thought (CoT) and ReAct (Agents).
§ LLM Application for Log analysis: increase productivity of SRE engineering by using LLMs and NLP techniques to analyze and understand issues in pipelines running in Kubernetes, EMR and others.

MLOpsLLMOpsGenerative AIPythonAWSKubernetes+1

Cisco

4 roles

Senior Software Engineering Manager

Promoted

Sep 2013 – Dec 2020 · 7 yrs 3 mos

§ Cloud Native Broadband Router ( https://www.cisco.com/c/en/us/products/routers/cloud-native-broadband-router/index.html ) is a virtual network function (VNF) implemented using a cloud native software stack (instead of a hardware platform) for better product agility, programmability, manageability, redundancy and feature velocity
§ Cisco Cross Work suite ( https://www.cisco.com/c/en/us/products/cloud-systems-management/crosswork-network-automation/index.html ) focusses on automation and analytics for managing large SP (Service Provider) infrastructures for offline and online network planning, network visibility and analytics, multi-layer visualization, point and click infrastructure management, closed loop automation, service assurance and others.
§ Cisco EPN (Evolved Programmable Network) Manager ( https://www.cisco.com/c/en/us/products/cloud-systems-management/evolved-programmable-network-epn-manager/index.html ) provides full lifecycle management (Design, Configuration, Visualization, Assurance/monitoring and Troubleshooting) for Carrier Ethernet services, Data center services.
§ Led a large development team, and as a manager of managers, building software products which scaled for managing large infrastructures. Held critical engineering ownerships of several key large Cisco customers in the Service Providers space.
Products built using Kubernetes, Docker, Microservices (java, go, python, javascript, java, C), Kafka, API Gateway, Timescale DB, Postgres, Cassandra, Redis, Prometheus, ElasticSearch/ELK, NodeJS, Spring boot, gRPC/Protobuf, Ansible , MongoDB, ElasticSearch/ELK, Postgres, Spark SQL, Spark Streaming, Angular UI, ReactJS, gRPC/Protobuf and others.

Cloud Native Broadband RouterCross Work SuiteEvolved Programmable Network ManagerKubernetesDockerMicroservices+1