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Auriga - Software Development Engineer II - Generative AI

Auriga - Software Development Engineer II - Generative AI

Auriga IT Consulting Pvt LtdJaipur
8 days ago
Job description

Description : About the job :

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 development - from 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)

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