Software Engineer - Integration Specialist

Scry AI; Hyderabad; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

SCRY AI is seeking a Forward Deployed AI/ML Software Engineer to design, build, and deploy GenAI and RAG applications. You will work with LLMs from OpenAI to open-source models, develop end-to-end pipelines, and deliver voice-enabled AI solutions across cloud and on‑prem environments.

You will implement robust Python backends, microservices, and REST APIs using FastAPI/Flask, with Docker-based deployments and strong collaboration with customers and data teams.

Qualifications

• 3+ years of software engineering experience with AI/ML focus.
• Strong Python backend development skills.
• Hands-on experience building and deploying GenAI/RAG solutions.

Responsibilities

• Design, develop, and deploy GenAI and RAG apps using LLMs (OpenAI, Claude, Llama, Qwen, etc.).
• Build complete RAG pipelines: ingestion, embeddings, vector DBs, retrieval, reranking, prompts, responses.
• Develop Voice AI/ Conversational AI integrations including ASR, TTS, APIs, and workflows.
• Create robust Python backends and APIs with FastAPI/Flask; Docker-based deployments.

Skills

Python

Backend development

GenAI

RAG

LangChain

LLMs

Docker

FastAPI

Tools

LangChain

LlamaIndex

Hugging Face

vector databases

embeddings

reranking

Job description

Experience: Software Development with AI/ML focus

Employment Type: Full-time

About Us

SCRY AI is an innovative AI-driven technology company focused on building intelligent, scalable, and high-performance solutions. We work with modern technologies across Software Engineering, AI/ML, and Data to solve real-world business problems and deliver impactful digital products. We are seeking a skilled Forward Deployed AI/ML Software Engineer with strong hands‑on experience in Generative AI, RAG, LLMs, backend engineering, and Voice AI. The candidate should combine strong software engineering skills with practical AI/ML expertise and be comfortable working directly with customers to build, integrate, deploy, troubleshoot, and optimize production AI solutions across varied environments.

Key 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-premise, 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.

Key Qualifications

• 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‑premise 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.

Good to Have

• Experience with Kubernetes, cloud‑native architectures, CI/CD, and production observability.
• Exposure to MCP, Agentic AI frameworks, AI evaluation, model optimization, and LLMOps/MLOps.
• Experience working with customer deployments, POCs, enterprise integrations, and troubleshooting in cloud/on‑premise environments.

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