Job description

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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