SDE II - ML/AI Engineer

Aurigait; Jaipur; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Aurigait is seeking a hands-on GenAI & Computer Vision Engineer based in Jaipur, India, with 3–5 years of experience in production-grade AI solutions. The role involves end-to-end model development, focusing on resolving challenges like LLM hallucinations and scalability in inference.

If you are proficient in LLM tools, containerization, and have a solid background in computer vision frameworks, you could be the right fit. Join us to tackle real-time inference constraints and engage in cross-functional collaboration.

Qualifications

• 3–5 years of experience in delivering production-grade AI solutions.
• Strong problem-solving mindset to debug LLM drift and model degradation.
• Excellent verbal and written communication skills.

Responsibilities

• Fine-tune and evaluate LLMs for specialized tasks.
• Develop and optimize computer vision models (YOLOv8, Mask R-CNN).
• Implement CI/CD for model/version management.

Skills

Proficient in LLM frameworks (Hugging Face Transformers, Ollama)

Experience with agent tools (LangChain, LangGraph)

Inference serving knowledge (Triton Inference Server)

Expertise in computer vision frameworks (PyTorch, TensorFlow)

Containerization skills (Docker, Kubernetes)

Model management tools (MLflow, DVC)

Cloud platform knowledge (AWS, GCP, Azure)

Programming in Python (required)

Education

Bachelor's or Master's in Computer Science, Electrical Engineering, AI/ML

Job description

We’re 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 development—from research and fine‑tuning through deployment, monitoring, and iteration. In this role, you’ll tackle domain‑specific challenges like LLM hallucinations, vector search scalability, real‑time inference constraints, and concept drift in vision models.

Key Responsibilities

• 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 auto‑scaling 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
  • TensorRT; ONNX Runtime; Torch‑TensorRT
• Container & Orchestration:
  • Docker and Kubernetes (KServe, SageMaker)
• Model Management:
  • MLflow or DVC
• Monitoring & Observability:
  • AWS (SageMaker, EC2/EKS) or GCP (Vertex AI, AI Platform) or Azure ML (AKS, ML Studio)
• Programming Languages:
  • Python (required); C++ or Go (preferred)

• 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