SPR

Syam Prasad Reddy

0%

Available for opportunities

Building
Intelligent
Systems.

Senior AI Engineer specializing in LLMs, multi-agent orchestration, Knowledge Graphs, RAG, and backend systems. I build production-grade AI platforms that automate enterprise workflows, power intelligent assistants, and deliver scalable, reliable solutions.

syamreddypenumallu@gmail.com
PythonLangChainFastAPIReactAWSOpenAIDockerPostgreSQLCrewAIPinecone
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01 — About

The mind behind the machines

From designing circuits to training neural networks — driven by one obsession: building things that work at scale.

S

Syam Prasad Reddy

Senior AI Engineer

Available for hire

I started with ECE circuits and signal processing — spending late nights debugging hardware that refused to cooperate. That systematic, first-principles thinking never left me, even when I switched lanes.

As an SDE-1, I shipped backend systems handling 500K+ daily requests. Then AI changed everything. I started building with LLMs on weekends, replaced entire workflows with a few thousand tokens, and never looked back.

Today as a Senior AI Engineer, I architect systems where agents think, tools execute, and results scale — while obsessing over sub-second latency and single-digit error rates.

"Good AI engineering isn't about the model — it's about the system around it."
IndiaMulti-Agent SystemsRAG at ScaleLLM Fine-Tuning

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Years of Experience

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AI Projects Shipped

0+

LLM Applications Built

0%

Uptime SLA Maintained

Core Expertise
AI / LLM Engineering95%
Backend & System Design88%
MLOps & AI Infrastructure82%
02 — Skills

The tech stack

Tools I use daily to build production AI systems — from model to infrastructure.

AI / ML · 12 skills
Python95%
LangChain92%
OpenAI API95%
LlamaIndex88%
Hugging Face85%
PyTorch78%
RAG Systems92%
Vector DBs88%
Prompt Engineering95%
Fine-tuning LLMs80%
CrewAI / AutoGen85%
Embeddings90%
LangChainOpenAIPineconeFastAPIPostgreSQLRedisDockerAWSReactNext.jsPyTorchCrewAI
03 — Projects

Things I've shipped

Production systems, not prototypes. Each project solved a real problem at real scale.

Featured
01

Autonomous AI Agent Platform

Multi-agent orchestration at production scale

Built a multi-agent orchestration platform where specialized AI agents collaborate to complete complex, multi-step tasks. Agents can search the web, write code, analyze data, and communicate with each other — all with human-in-the-loop oversight.

70% time saved500+ users99.9% uptime
PythonLangChainCrewAIFastAPIRedisPostgreSQLDocker
Details
Featured
02

Intelligent RAG Document Engine

Semantic search over 10M+ enterprise documents

Engineered a production-grade Retrieval-Augmented Generation system that enables natural language querying over massive document corpora. Features hybrid search, re-ranking, and citation-aware responses with hallucination detection.

10M+ docs<800ms p9994% accuracy
PythonLlamaIndexPineconeOpenAIFastAPI+1
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Featured
03

AI-Powered API Gateway

Smart routing, caching & rate limiting with ML

Designed and shipped a next-generation API gateway that uses ML models for intelligent request routing, semantic response caching, and anomaly detection. Learns traffic patterns to auto-scale and prevent abuse.

60% cost reduction$40K/mo saved2M req/day
GoPythonRedisKubernetesPrometheus+1
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04

Real-Time AI Analytics Dashboard

Streaming ML insights at millisecond latency

Full-stack analytics platform with real-time ML inference on streaming data. Features anomaly detection, predictive forecasting, and natural language report generation — all updating live as data flows in.

<100ms latency1M events/day10 sec reports
PythonKafkaFastAPIWebSocketsNext.js+2
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05

LLM Fine-Tuning Infrastructure

Custom model training pipeline for domain tasks

Built end-to-end fine-tuning infrastructure for training domain-specific LLMs. Includes dataset curation, PEFT/LoRA training, evaluation harness, and one-click deployment to inference endpoints.

89% domain accuracy+27pts vs GPT-42hr train time
PythonPyTorchPEFTHugging FaceAWS SageMaker+1
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04 — Experience

The journey

From ECE classrooms to production AI systems — every step shaped how I think about scale.

Senior AI Engineer

Beacon.li

Full-time
2026 Apr — Present
Remote

Leading AI product development — architecting LLM-powered features, multi-agent systems, and intelligent backend infrastructure that powers thousands of daily active users.

  • Architected and developed an enterprise AI agent platform supporting multi-step autonomous workflows and tool orchestration.
  • Designed multi-level AI orchestrators to coordinate specialized agents, enabling complex end-to-end enterprise automation.
  • Built Knowledge Graph-powered reasoning systems to improve contextual understanding, entity relationships, and AI response accuracy.
  • Developed scalable RAG pipelines integrating vector databases, enterprise search, and structured knowledge sources.
  • Designed AI copilots and intelligent automation solutions for enterprise platforms including HR, operations, and business workflows.
  • Collaborated directly with enterprise clients to gather requirements, design AI solutions, conduct technical demos, and drive successful production deployments.
  • Led architecture discussions, mentored engineers, established AI engineering standards, and guided best practices for scalable AI development.
PythonLangChainFastAPIPostgreSQLRedisAWSDocker

Software Development Engineer I

Beacon.li

Full-time
2024 Aug — 2026 Apr
Hyderabad

Joined as an early-career engineer and quickly took ownership of backend services and API development. Shipped critical features that scaled from zero to production.

  • Designed and developed AI agents for Darwinbox, greytHR, and Keka, enabling users to resolve HR queries through conversational AI and significantly reducing support tickets.
  • Built enterprise search and analytics capabilities using DataSearches, allowing users to retrieve HR, payroll, leave, and organizational insights in seconds.
  • Developed backend APIs, agent orchestration workflows, and automation services using Node.js and JavaScript for enterprise-scale applications.
  • Implemented AI-powered Leave Policy Creation and Analysis agents capable of extracting, validating, and configuring HR policies from documents.
  • Integrated LLM-based features with enterprise systems, improving user productivity through intelligent search, recommendations, and workflow automation.
  • Collaborated with cross-functional teams to deliver production-ready AI solutions and integrations for multiple enterprise HR platforms.
Node.jsPostgreSQLRedisDockerAWSReact

B.Tech in ECE

University

Education
2019 — 2023
On-campus

Electronics and Communication Engineering — the foundation that shaped my analytical thinking and systems-level perspective. Where hardware intuition met software ambition.

  • Graduated with distinction; final-year project on ML-based signal classification achieved 94% accuracy
  • Built IoT + ML project that won Best Innovation Award at the university tech fest
  • Led the coding club, organizing hackathons that attracted 500+ participants
  • Self-taught Python, ML fundamentals, and web development during academic years
PythonMATLABC++TensorFlowIoTSignal Processing
05 — AI Work

Where AI meets engineering

My core differentiation — I don't just use AI models, I build the infrastructure and orchestration layers that make them reliable, fast, and cost-efficient in production.

500K+

LLM Calls / Day

<800ms

Avg Latency

60%

Cost Saved

Multi-Agent Systems

Designing autonomous agent networks where specialized AI models collaborate, delegate subtasks, and self-correct — enabling end-to-end automation of complex workflows.

  • Task decomposition & orchestration
  • Agent-to-agent communication
  • Tool use & function calling
  • Human-in-the-loop checkpoints

RAG & Knowledge Systems

Building production-grade RAG pipelines that make LLMs grounded, accurate, and explainable — from chunking strategies to hybrid retrieval and re-ranking.

  • Hybrid vector + keyword search
  • Multi-hop reasoning chains
  • Hallucination detection & mitigation
  • Citation-aware response synthesis

LLM Fine-Tuning

Training domain-specific models using PEFT/LoRA techniques that outperform GPT-4 on narrow tasks — at a fraction of the inference cost.

  • LoRA / QLoRA fine-tuning
  • Dataset curation & synthesis
  • RLHF-style preference optimization
  • Evaluation harness design

AI Infrastructure & MLOps

Building the pipes that make AI reliable in production — observability, semantic caching, versioning, rollback, and cost optimization at scale.

  • LLMOps & prompt versioning
  • Semantic response caching
  • Token cost optimization
  • A/B testing for AI features

Typical AI System Architecture

User Input01
LLM Router02
Vector Store03
Agent Layer04
Tool Calls05
Response06
06 — Recognition

Milestones & wins

2026

Claude Foundation Certified Architect

Anthropic

Earned the Claude Foundation Certified Architect certification, demonstrating expertise in designing and building AI-powered applications using Claude.

2024

60% Cost Reduction

Production Achievement

Cut LLM API costs by 60% through semantic caching

2024

Top 5% GitHub

Open Source

Contributed to 3 major AI open-source repositories

2024

0→Production in 6 Weeks

Startup Milestone

Shipped the core AI platform from scratch to production

"

From debugging circuit boards at 2am to architecting AI systems that handle half a million requests daily — the obsession with building things that actually work never changed.

S

Syam Prasad Reddy

Senior AI Engineer

07 — Contact

Let's build something remarkable

Have an interesting AI problem, a startup idea, or just want to talk shop? I'm always open to the right conversation.

Email

syamreddypenumallu@gmail.com

GitHub

@syamreddy

LinkedIn

in/syamprasadreddy

Open to opportunities

Full-time roles, consulting, or interesting collaborations