Sr Data Engineers - AI/ML - Chennai
At a glance
Senior AI Engineering role at Fa Ertb Saasfaprod1. India.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build multi-agent AI workflows with LangGraph and develop retrieval systems using LlamaIndex and vector databases
- Choose and connect commercial or open-source language models based on their performance and cost
- Create scalable backend services with FastAPI and asynchronous Python, then package them in Docker for production
- Fine-tune language models with PyTorch or TensorFlow to improve performance on specific domains
- Improve AI workflows by tracking latency, cost and reliability, using prompt design and monitoring tools
What you bring
- You have built and launched generative AI applications for production use
- You are proficient in Python and current AI libraries such as LangChain or LlamaIndex
- You have worked with vector search, embeddings and advanced retrieval methods
- You understand model fine-tuning and how to quantize or host local language models
- You are familiar with production monitoring, API security and ML CI/CD
Who this fits
This senior role suits an engineer who combines machine learning knowledge with production software development. The position is in Chennai, India, and is not open to independent contractors.
From the employer
Role Summary:
We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.
- Technical Stack:
- LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.
- Frameworks: LangChain, LlamaIndex, and Hugging Face.
- Orchestration: LangGraph and Multi-Agent Systems (MAS).
- Development: Python, FastAPI, and Asynchronous Programming.
- RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.
- ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.
- Deployment: Docker, Production API management, and LLM monitoring.
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Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.
Key Responsibilities:
- Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.
- Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.
- Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.
- Design and deploy high-performance, scalable backend services using FastAPI and Async Python.
- Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.
- Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.
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Containerize and deploy AI services via Docker to production environments.
Required Qualifications:
- Hands-on experience building and deploying GenAI applications in a production setting.
- Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).
- Experience with vector search, embedding models, and advanced data retrieval patterns.
- Knowledge of model fine-tuning techniques and local LLM quantization/hosting.
- Familiarity with production-grade monitoring, API security, and CI/CD for ML.
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Compensation, Benefits and Duration
Minimum Compensation: USD
Maximum Compensation: USD
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post