Auriga - Generative AI & Computer Vision Engineer

Description :

Were seeking a hands-on GenAI & Computer Vision Engineer with 35 years of experience delivering production-grade AI solutions.

You must be fluent in the core libraries, tools, and cloud services listed below, and able to own end-to-end model developmentfrom research and fine-tuning through deployment, monitoring, and iteration.

In this role, youll tackle domain-specific challenges like LLM hallucinations, vector search scalability, real-time inference constraints, and concept drift in vision models.

Key Responsibilities :

Generative AI & LLM Engineering :

- Fine-tune and evaluate LLMs (Hugging Face Transformers, Ollama, LLaMA) for specialized tasks.

- Deploy high-throughput inference pipelines using vLLM or Triton Inference Server.

- Design agent-based workflows with LangChain or LangGraph, integrating vector databases (Pinecone, Weaviate) for retrieval-augmented generation.

- Build scalable inference APIs with FastAPI or Flask, managing batching, concurrency, and rate-limiting.

Computer Vision Development :

- Develop and optimize CV models (YOLOv8, Mask R-CNN, ResNet, EfficientNet, ByteTrack) for detection, segmentation, classification, and tracking.

- Implement real-time pipelines using NVIDIA DeepStream or OpenCV (cv2); optimize with TensorRT or ONNX Runtime for edge and cloud deployments.

- Handle data challengesaugmentation, domain adaptation, semi-supervised learningand mitigate model drift in production.

MLOps & Deployment :

- Containerize models and services with Docker; orchestrate with Kubernetes (KServe) or AWS SageMaker Pipelines.

- Implement 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 Collaboration :

- Define SLAs for latency, accuracy, and throughput alongside product and DevOps teams.

- Evangelize best practices in prompt engineering, model governance, data privacy, and interpretability.

- Mentor junior engineers on reproducible research, code reviews, and end-to-end AI delivery.

Required Qualifications :

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).

Additionally :

- Bachelors or Masters in Computer Science, Electrical Engineering, AI/ML, or a related field.

years of professional experience shipping both generative and vision-based AI models in production.

- Strong problem-solving mindset; ability to debug issues like LLM drift, vector index staleness, and model degradation.

- Excellent verbal and written communication skills.

Typical Domain Challenges Youll Solve :

- LLM Hallucination & Safety : Implement grounding, filtering, and classifier layers to reduce false or unsafe outputs.

- Vector DB Scaling : Maintain low-latency, high-throughput similarity search as embeddings grow to millions.

- Inference Latency : Balance batch sizing and concurrency to meet real-time SLAs on cloud and edge hardware.

- Concept & Data Drift : Automate drift detection and retraining triggers in vision and language pipelines.

- Multi-Modal Coordination : Seamlessly orchestrate data flow between vision models and LLM agents in complex workflows.