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Job Description
Description The AI/ML Engineer will design, build, and deploy production-grade machine learning and AI systems that power core products and features. This role bridges cutting-edge research with reliable, scalable engineering, turning prototypes into high-performance services that run 24/7 in production.
Key Responsibilities:
+ Design and implement end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, deployment, and monitoring + Develop, optimize, and productionize models using PyTorch/TensorFlow/JAX (including LLMs, vision, multimodal, and custom architectures) + Optimize inference for latency, memory, and cost (quantization, pruning, distillation, TensorRT, ONNX, vLLM) + Integrate models into backend systems via REST/gRPC APIs, event-driven architectures, or real-time serving + Own MLOps practices: experiment tracking (MLflow, W& B), model registry, CI/CD for ML, canary deployments, drift detection, and observability + Collaborate with data scientists to harden research prototypes into clean, tested, production-ready code + Build and maintain retrieval-augmented generation (RAG), agentic workflows, and prompt-engineered systems when appropriate (LangChain, LlamaIndex) + Continuously monitor, retrain, and improve live models to maintain performance and reliability Requirements + Bachelor's or Master's in Computer Science, Engineering, Mathematics, or equivalent experience + 3-8+ years of hands-on experience shipping ML/AI systems to production + Expert-level Python and deep proficiency in PyTorch (preferred) or TensorFlow/JAX + Proven track record with modern ML infrastructure: Docker, Kubernetes, Ray, Triton Inference Server, cloud ML platforms (SageMaker, Vertex AI, Bedrock) + Strong MLOps experience (MLflow, Airflow, feature stores, model registries, monitoring tools) + Solid software engineering fundamentals: testing, code reviews, system design, versioning + Experience integrating models into larger systems (FastAPI, microservices, streaming pipelines)
