SDE II - ML/AI Engineer

We're seeking a hands-on GenAI & Computer Vision Engineer with 3–5 years of experience delivering production-grade AI solutions. You must be fluent in the core libraries, tools, and cloud services listed above, and able to own end-to-end model development—from research and fine-tuning through deployment, monitoring, and iteration. In this role, you'll tackle domain-specific challenges like LLM hallucinations, vector search scalability, real-time inference constraints, and concept drift in vision models.Key ResponsibilitiesGenerative AI & LLM EngineeringFine-tune and evaluate LLMs (Hugging Face Transformers, Ollama, LLaMA) for specialized tasksDeploy high-throughput inference pipelines using vLLM or Triton Inference ServerDesign agent-based workflows with LangChain or LangGraph, integrating vector databases (Pinecone, Weaviate) for retrieval-augmented generationBuild scalable inference APIs with FastAPI or Flask, managing batching, concurrency, and rate-limitingComputer Vision DevelopmentDevelop and optimize CV models (YOLOv8, Mask R-CNN, ResNet, EfficientNet, ByteTrack) for detection, segmentation, classification, and trackingImplement real-time pipelines using NVIDIA DeepStream or OpenCV (cv2); optimize with TensorRT or ONNX Runtime for edge and cloud deploymentsHandle data challenges—augmentation, domain adaptation, semi-supervised learning—and mitigate model drift in productionMLOps & DeploymentContainerize models and services with Docker; orchestrate with Kubernetes (KServe) or AWS SageMaker PipelinesImplement CI/CD for model/version management (MLflow, DVC), automated testing, and performance monitoring (Prometheus + Grafana)Manage scalability and cost by leveraging cloud autoscaling on AWS (EC2/EKS), GCP (Vertex AI), or Azure ML (AKS)Cross-Functional CollaborationDefine SLAs for latency, accuracy, and throughput alongside product and DevOps teamsEvangelize best practices in prompt engineering, model governance, data privacy, and interpretabilityMentor junior engineers on reproducible research, code reviews, and end-to-end AI delivery

You must be proficient in at least one tool from each category below: LLM Frameworks & Tooling:Hugging Face Transformers, Ollama, vLLM, or LLaMA Agent & Retrieval Tools:LangChain or LangGraph; RAG with Pinecone, Weaviate, or Milvus Inference Serving:Triton Inference Server; FastAPI or Flask Computer Vision Frameworks & Libraries:PyTorch or TensorFlow; OpenCV (cv2) or NVIDIA DeepStream Model Optimization:TensorRT; ONNX Runtime; Torch-TensorRT MLOps & Versioning:Docker and Kubernetes (KServe, SageMaker); MLflow or DVC Monitoring & Observability: Prometheus; Grafana Cloud Platforms:AWS (SageMaker, EC2/EKS) or GCP (Vertex AI, AI Platform) or Azure ML (AKS, ML Studio) Programming Languages: Python (required); C++ or Go (preferred)