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.35 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.(ref : hirist.tech)
