Artificial Intelligence Engineer

💻 Position: AI ML Engineer

📍Location: Indiranagar, bengaluru

🗓️ Working Days: 5 days

⏰ Work Timings: 10am -7pm

📌 Experience Required: 2 -6 years

🤝 Client: Leading B2B Fintech Startup

💸 Salary: 30 LPA - 40 LPA

Job Description :

Build with LLMs & GenAI :

• Fine-tune and deploy Large Language Models (LLMs) like GPT, BERT, etc., for

core AI features across the product

• Develop and optimize Generative AI capabilities — diffusion models, LoRA, or

similar frameworks

• Continuously improve performance using prompt engineering, retrieval-augmented

generation (RAG), and user feedback

Deploy AI Workflows :

• Build scalable, production-ready AI workflows and pipelines using APIs,

microservices, and vector databases

• Translate prototypes into stable product features in collaboration with product and

backend teams

• Integrate AI systems into real-time and batch data processing layers

Infrastructure & Performance

• Work with cloud GPU environments (AWS/GCP/Azure) to train, run, and monitor

LLM-based systems :

• Optimize inference latency and throughput for high-scale use cases

Requirements for the Role :

• 2–6 years of experience in AI/ML Engineering

• Strong Python skills with experience in frameworks like PyTorch or TensorFlow

• Proven hands-on experience with LLMs, prompt engineering, or Generative AI

models

• Experience deploying models to production in a cloud environment

(AWS/GCP/Azure)

• Comfortable with tools like HuggingFace, LangChain, or similar open-source stacks

Core Expertise

• Experience working with transformer-based LLMs (e.g., GPT, BERT, Falcon,

LLaMA).

• Strong foundation in ML algorithms, deep learning, and model deployment.

• Proven track record in Generative AI and MLOps.

Technical Stack :

• Languages: Python (must-have), bonus for Golang or Node.js.

• Frameworks: TensorFlow, PyTorch, Hugging Face.

• Infra & DevOps: AWS/GCP/Azure, Docker, Kubernetes.

• Pipelines: MLflow, Kubeflow, Airflow.