Artificial Intelligence Engineer
On-site
Transient.AI
Startup
Product
B2B
₹ 10-30 Lacs PA
Series A
Information Technology
Bangalore, Karnataka, India
Post Status: Active
Permanent
31 applications
Experience: 5-8 Years
Skills
Docker
Application Programming Interfaces (API)
Python
Tensorflow
AWS
PyTorch
MLOps
Prompt Engineering
Kubernetes
LLM
GenAI
Hugging Face
RAG
Vector Databases
LangChain
Fine-tuning
Posted 75 days ago

About the job

We are looking for an AI/ML Engineer with 5+ years of experience building and deploying machine learning and AI solutions using Python and modern frameworks such as PyTorch, TensorFlow, Hugging Face, or LangChain. The ideal candidate has hands-on experience with LLMs, RAG, MLOps, and enjoys working in a fast-paced, high-ownership environment to build scalable AI applications.

Key Responsibilities

• Design, build, and deploy machine learning and AI models that power Transient.AI's core products (research automation, document intelligence, investor matching, and workflow orchestration).

• Work on applied NLP/LLM systems, including retrieval-augmented generation, structured extraction from unstructured financial documents, and model evaluation pipelines.

• Partner closely with product and founding engineers to translate capital markets workflows into scalable AI systems.

• Own model performance, reliability, and cost — from experimentation through production deployment.

• Build and maintain data pipelines, feature stores, and evaluation frameworks to support rapid iteration.

• Ensure systems meet the compliance, auditability, and security standards required in regulated financial environments.

• Collaborate with a distributed team across New York, Miami, Singapore, and India.

What We're Looking For

• 5+ years of experience building and deploying machine learning or AI systems in production.

• Strong hands-on experience with Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent).

• Experience with LLMs — fine-tuning, prompt engineering, RAG architectures, or agentic systems — is highly valued.

• Solid grounding in data structures, distributed systems, and MLOps practices (model serving, monitoring, versioning).

• Prior experience at a strong product company, high-growth startup, or a top-tier engineering background (IIT or equivalent) preferred.

• Comfort operating in an early-stage, high-ownership environment with limited process and high ambiguity.

• Exposure to fintech, capital markets, or other regulated industries is a plus, though not mandatory.