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Open nowPosted 21 days ago

Software Engineer - Integration Specialist

Scry AI22 open roles

Where
Noida
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Your applicationOpen nowSoftware Engineer - Integration SpecialistScry AI · Noida

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The clock on this job

Early applications get read.

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Share of postings closed within
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  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 21 days ago

The posting

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-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.

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-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.

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-premises environments.
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