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

- 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 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