Software Engineer - Integration Specialist

Scry Analytics; Dadri, Hyderabad, Gurugram District; Híbrido; INR 2.500.000 - 3.500.000; Jornada completa

Descripción de la vacante

Scry Analytics is seeking a hands-on Software Engineer with GenAI and RAG expertise to design, build, and deploy scalable AI applications. You will develop end-to-end solutions spanning data ingestion, embeddings, vector databases, and retrieval, while integrating voice-enabled AI and backend services.

The role involves working with LangChain and LlamaIndex, deploying via Docker, and collaborating with customers to translate business needs into implementable AI solutions.

Formación

• 3+ years of experience in Software Engineering, AI/ML Engineering, Data Science, or GenAI, with hands-on development.
• Strong proficiency in Python, backend development, and software engineering fundamentals.
• Hands-on experience building and deploying RAG-based applications and LLM solutions.
• Experience with LangChain, LlamaIndex, Hugging Face, vector databases, embeddings, and reranking.
• Good understanding of LLMs, prompt engineering, Agentic AI, tool calling, and workflow automation.
• Experience with Voice AI/Voice Bots (ASR, TTS, telephony, real-time pipelines).
• Experience with PostgreSQL, MongoDB, Redis, REST APIs, and backend integrations.
• Strong experience with FastAPI/Flask, microservices, Docker, and backend architecture; Kubernetes is a plus.
• Experience with AWS/Azure/GCP and cloud or on-prem deployments.
• Knowledge of CI/CD, automated testing, Git, logging, monitoring, production debugging, and AI/ML inference optimization.
• Strong problem-solving, communication, customer-facing, and collaboration skills.

Responsabilidades

• Design, develop, and deploy scalable GenAI and RAG applications using OpenAI, Claude, Llama, Qwen, and other models.
• Build complete RAG pipelines including document ingestion, chunking, embeddings, vector databases, retrieval, reranking, prompt engineering, and response generation.
• Develop and integrate Voice Bot / Conversational AI solutions connecting ASR, LLM/SLM, TTS, telephony, APIs, and workflows.
• Build robust Python backend services and APIs using FastAPI/Flask.
• Develop Agentic AI and workflow automation using LangChain/LlamaIndex, including tool calling and external integrations.
• Integrate AI applications with SQL/NoSQL databases, APIs, enterprise systems, and customer data sources.
• Collaborate with customers and stakeholders to translate business needs into technical implementations.
• Take AI/ML solutions from POC to production, focusing on scalability, reliability, latency, monitoring, and maintainability.
• Deploy and troubleshoot AI applications using Docker across cloud, on-premises, and customer environments.
• Optimize AI applications for latency, throughput, cost, model performance, and resource utilization.
• Follow strong software engineering practices including Git, CI/CD, testing, logging, monitoring, code reviews, and documentation.
• Work with Data Scientists, ML Engineers, Product, Infrastructure, and customer teams to deliver production-ready AI solutions.

Conocimientos

Python

Backend development

RAG applications

LLMs

LangChain

LlamaIndex

Hugging Face

Vector databases

Embeddings

Prompt engineering

Agentic AI

Tool calling

Workflow automation

SQL

NoSQL

PostgreSQL

MongoDB

Redis

FastAPI

Flask

Docker

Kubernetes

AWS

Azure

GCP

CI/CD

Testing

Git

Monitoring

Production debugging

AI inference optimization

Herramientas

OpenAI

Claude

Llama

Qwen

Descripción del empleo

Role & responsibilities

• Design, develop, and deploy scalable GenAI and RAG applications using LLMs such as OpenAI, Claude, Llama, Qwen, and other open-source models.


• Build complete RAG pipelines including document ingestion, chunking, embeddings, vector databases, retrieval, reranking, prompt engineering, and response generation.


• Develop and integrate Voice Bot / Conversational AI solutions connecting ASR, LLM/SLM, TTS, telephony, APIs, and business workflows.


• Build robust Python backend services and APIs using frameworks such as FastAPI/Flask.


• Develop Agentic AI and workflow automation solutions using frameworks such as LangChain/LlamaIndex, including tool calling and external system integrations.


• Integrate AI applications with SQL/NoSQL databases, APIs, enterprise systems, and customer-specific data sources.


• Work directly with customers and stakeholders to understand requirements, troubleshoot issues, demonstrate solutions, and translate business needs into technical implementations.


• Take AI/ML solutions from POC to production, focusing on scalability, reliability, latency, monitoring, and maintainability.


• Deploy and troubleshoot AI applications using Docker across cloud, on-premises, and customer environments.


• Optimize AI applications for latency, throughput, cost, model performance, and resource utilization.


• Follow strong software engineering practices including Git, CI/CD, testing, logging, monitoring, code reviews, and documentation.


• Collaborate with Data Scientists, ML Engineers, Product, Infrastructure, and customer teams to deliver and support production-ready AI solutions.

Preferred candidate profile

• 3+ years of experience in Software Engineering, AI/ML Engineering, Data Science, or GenAI, with strong hands-on development experience.


• Strong proficiency in Python, backend development, and software engineering fundamentals.


• Strong hands-on experience building and deploying RAG-based applications and LLM solutions.


• Experience with LangChain, LlamaIndex, Hugging Face, vector databases, embeddings, reranking, or equivalent technologies.


• Good understanding of LLMs, prompt engineering, Agentic AI, tool calling, MCP, and workflow automation.


• Experience with Voice AI / Voice Bots, preferably including ASR, TTS, telephony, and real-time conversational pipelines.


• Experience with PostgreSQL/SQL, MongoDB, Redis, or similar databases, along with REST APIs and backend integrations.


• Strong experience with FastAPI/Flask, microservices, Docker, and backend architecture; Kubernetes is a plus.


• Experience with AWS/Azure/GCP and cloud or on-premises deployments.


• Knowledge of CI/CD, automated testing, Git, logging, monitoring, production debugging, and AI/ML inference optimization.


• Strong problem-solving, communication, customer-facing, and collaboration skills, with the ability to work independently in fast-paced environments.