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Auriga It
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ML / AI Engineer

ML / AI Engineer

Auriga It

3 - 5 years

9 - 13 Lacs

Jaipur

Posted:1 week ago| Platform:

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Skills Required

python docker tensorflow pytorch aws kubernetes cloud services triton c++ cnn natural language processing neural networks aws sagemaker aiml machine learning artificial intelligence deep learning computer vision keras flask onnx opencv ml

Work Mode

Work from Office

Job Type

Full Time

Job Description

Job Summary

Were 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 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
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 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

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ML / AI Engineer

Experience & Salary

Skills Required

Work Mode

Job Type

Job Description

Job Summary

must

Key Responsibilities

Generative AI & LLM Engineering

Computer Vision Development

MLOps & Deployment

Cross-Functional Collaboration

Required Qualifications

must

LLM Frameworks & Tooling:

Agent & Retrieval Tools:

Inference Serving:

Computer Vision Frameworks & Libraries:

Model Optimization:

MLOps & Versioning:

Monitoring & Observability:

Cloud Platforms:

or

or

Programming Languages:

Typical Domain Challenges Youll Solve

LLM Hallucination & Safety:

Vector DB Scaling:

Inference Latency:

Concept & Data Drift:

Multi-Modal Coordination:

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