Software Engineer - Integration Specialist

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.