Data Scientist-Data Science-Gen AI Engineer

• Lead the architecture, development, and deployment of scalable machine learning systems, focusing on real-time inference for LLMs serving multiple concurrent users.


• Optimize inference pipelines using high-performance frameworks like vLLM, Groq, ONNX Runtime, Triton Inference Server, and TensorRT to minimize latency and cost.


• Design and implement agentic AI systems utilizing frameworks such as LangChain, AutoGPT, and ReAct for autonomous task orchestration.


• Fine-tune, integrate, and deploy foundation models including GPT, LLaMA, Claude, Mistral, Falcon, and others into intelligent applications.


• Develop and maintain robust MLOps workflows to manage the full model lifecycle including training, deployment, monitoring, and versioning.


• Collaborate with DevOps teams to implement scalable serving infrastructure leveraging containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure).


• Implement retrieval-augmented generation (RAG) pipelines integrating vector databases like FAISS, Pinecone, or Weaviate.


• Build observability systems for LLMs to track prompt performance, latency, and user feedback.


• Work cross-functionally with research, product, and operations teams to deliver production-grade AI systems handling real-world traffic patterns.


• Stay updated on emerging AI trends, hardware acceleration techniques, and contribute to open-source or research initiatives where possible.

Required:

• Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Machine Learning, or related fields.


• 6 7 years of experience in machine learning engineering, applied AI, or MLOps roles.


• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.


• Deep knowledge of NLP, transformer-based architectures, and generative AI models.


• Hands-on experience with scalable LLM inference optimization using tools like vLLM, Groq, Triton Inference Server, TensorRT, or ONNX Runtime.


• Proven ability to serve AI models to concurrent users with low latency and high throughput.


• Experience in deploying ML systems on cloud platforms (AWS, GCP, Azure).


• Expertise in containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.


• Familiarity with vector search technologies (FAISS, Pinecone, Weaviate) and RAG implementations.

Preferred:

• Experience with agent-based AI frameworks, autonomous workflows, and prompt chaining.


• Knowledge of fine-tuning methods like LoRA, PEFT, RLHF.


• Contributions to open-source AI/ML projects or active participation in AI research communities.


• Understanding of hardware acceleration for GPU/TPU inference optimization.


• Exposure to event-driven and streaming data systems like Kafka or Redis Streams.

• Architect and deploy scalable LLM-powered applications with low-latency, high-throughput inference.


• Optimize model serving using state-of-the-art tools such as vLLM, Groq, ONNX Runtime, Triton, and TensorRT.


• Design and develop agentic AI frameworks and autonomous workflows using LangChain, AutoGPT, and ReAct.


• Fine-tune and integrate large foundation models (GPT, Claude, LLaMA, etc.) into production systems.


• Build and maintain MLOps pipelines covering training, deployment, monitoring, and lifecycle management.


• Collaborate with DevOps for containerized deployment and scalable orchestration using Kubernetes and Docker.


• Implement and optimize retrieval-augmented generation (RAG) solutions using vector databases like FAISS, Pinecone, or Weaviate.


• Monitor and evaluate model performance, latency, and user interaction metrics to ensure system reliability and efficiency.


• Work closely with cross-functional teams to translate AI research into practical, scalable solutions.