AI Software Engineer – Python, LLM Integrations & Scalable Systems

We are seeking a hands-on AI Software Engineer to design, build, and deploy intelligent backend systems that power conversational AI, automation, and data-driven decision engines.

You’ll collaborate with data scientists, ML engineers, and product teams to integrate LLM-based models (OpenAI, Anthropic, Meta Llama, etc.) into scalable microservices and internal tools.

Key ResponsibilitiesDesign and develop Python-based backend systems supporting AI/LLM workflows, APIs, and data pipelines. Build scalable microservices and vector-database integrations (e.g., Milvus, Pinecone, FAISS) for retrieval-augmented generation (RAG) pipelines.

Integrate and orchestrate LLMs using APIs (OpenAI, Anthropic, Hugging Face, vLLM, Triton, or similar). Work closely with data engineering to optimize data ingestion, preprocessing, and embeddings pipelines.

Implement asynchronous and distributed processing (Celery, Kafka, or Ray). Deploy and monitor services on Docker/Kubernetes with CI/CD pipelines (GitHub Actions, Jenkins, or GitLab CI). Maintain documentation, testing, and model performance metrics.

Collaborate with DevOps and security to ensure safe and reliable AI deployments. Required Skills & Experience3+ years experience in backend or full-stack development with Python (FastAPI, Flask, or Django).

Proven experience integrating AI/ML or NLP systems (LLMs, embeddings, transformers, etc.). Strong understanding of RESTful and async APIs, data serialization, and model inference optimization.

Familiarity with vector databases (Milvus, Pinecone, FAISS, Weaviate) and document chunking/embedding techniques. Experience with SQL and NoSQL databases (PostgreSQL, MongoDB, Redis). Hands-on with Docker, Kubernetes, and cloud environments (AWS / GCP / Azure).

Knowledge of MLOps workflows (model packaging, inference serving, versioning). Experience with Git, CI/CD, and automated testing. Nice to HaveFamiliarity with AI voice technologies (Riva, ElevenLabs, VAPI SDK, or similar).

Experience with LangChain, LlamaIndex, or Haystack for RAG pipelines. Exposure to NVIDIA Triton / TensorRT-LLM / vLLM for high-performance inference. Understanding of prompt engineering, retrieval evaluation, and fine-tuning pipelines.

Experience contributing to open-source AI frameworks. Why Join UsBuild real AI products — from voice agents to LLM-powered automation systems — not just prototypes.

Work with a high-performance engineering team using NVIDIA hardware and cutting-edge open-source tools. 100% remote flexibility, cross-functional collaboration, and ownership of critical AI systems.