Job Summary :

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 below, 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 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 challenges - augmentation, domain adaptation, semi-supervised learning - and 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)
- Bachelor's or Master's in Computer Science, Electrical Engineering, AI/ML, or a related field
- 3 - 5 years of professional experience shipping both generative and vision-based AI models in production
- Solid 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 You'll 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

📌 Generative AI & Computer Vision Engineer (India)
🏢 Auriga IT Consulting Private
📍 India