ML/AI Engineer

Job Summary

must

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

must

• 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:

or

• Programming Languages:

Python (required); C++ or Go (preferred)

Additionally:

• 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

About Company

Hi there! We are Auriga IT.

We power businesses across the globe through digital experiences, data and insights. From the apps we design to the platforms we engineer, we're driven by an ambition to create world-class digital solutions and make an impact. Our team has been part of building the solutions for the likes of Zomato, Yes Bank, Tata Motors, Amazon, Snapdeal, Ola, Practo, Vodafone, Meesho, Volkswagen, Droom and many more.

We are a group of people who just could not leave our college-life behind and the inception of Auriga was solely based on a desire to keep working together with friends and enjoying the extended college life.

Who Has not Dreamt of Working with Friends for a Lifetime

Come Join In

Our Website - https://aurigait.com/