ML/AI Engineer

Job Summary

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