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AI Engineer
Job Description
What You'll Do
- Architect AI Infrastructure: Design and build backend services, data pipelines, and orchestration workflows for LLMs and agent-based systems.
- Ship Production-Grade Code: Write high-performance, clean, testable code to serve high-throughput AI workflows and data-driven systems.
- Integrate & Scale: Build resilient APIs and pipelines integrating foundational models, vector data stores, and backend microservices across cloud environments.
- Drive Productization: Translate ambiguous enterprise client problems into robust, scalable platform components and reusable AI products.
- Cross-Functional Collaboration: Partner directly with ML engineers, product leads, and frontend developers to rapidly ship cutting-edge AI capabilities.
- Core Backend Mastery: Strong problem-solving and analytical skills with solid fundamentals in modern backend technologies (Python, Go, Java, or Node.js).
- System Architecture: Solid understanding of data structures, REST/gRPC APIs, SQL/NoSQL databases, and distributed systems design.
- AI/ML Technical Exposure: Hands-on experience or direct exposure to LLMs, RAG frameworks (e.g., LangChain, LlamaIndex), model integrations, or AI/ML data pipelines.
- Production Operations: Proven track record of building, deploying, operating, and scaling backend systems in production cloud environments.
- Problem-Solving Mindset: Ability to take ambiguous business and technical requirements and turn them into robust, clean software solutions.
More Info
Key Skills
About Company
Skills:
Integrations, Logging, Authentication, tracing, escalation workflows, LLM tool function calling, multi-turn conversational workflows, structured outputs, Caching, backend APIs, Authorization, automated regression tests, queues, context management, AI agents, SQL databases, knowledge systems, observability
Skills:
probability , tokenization , production deployment , Apis, Neural Networks, Containers, Python, AWS, embeddings, data pipelines, Transformers, LLMs, vector databases, reinforcement learning, orchestration layers, retrieval systems, agentic workflows, Optimization, RAG, Statistics, backend services, linear algebra
Skills:
Javascript, Python
Skills:
Algorithms, Machine Learning, data structures, Python, AI ML solutions, Generative AI, AI concepts, software development concepts, LLM-based applications
Skills:
C++, Cloud Infrastructure, Pytorch, Python, Large-scale retrieval, On-device deployment, Evaluation harnesses, Go, AI-assisted coding, TensorRT, Machine learning models, Video understanding, VLM embedding models, TFLite, ONNX Runtime, SNPE, LLM or agent-based applications, Annotation pipelines, Agentic development tools, Distributed training