Tror - AI for everyone

Job Title: Data Scientist (In-Person Interview) 
 
Location: Remote 
 
Duration: Long-Term 
 
Visa: Any visa is Fine(Need 4+ years of experience in Data Scientist) 
 
Interview process: 1_st_ round virtual and 2_nd_ round (F2F mandatory) 
 
End Client: Working in Implementation Project 
 
Note: Need at least 4+ years of experience complete into Data Scientist. 
 
Note: while sharing the resume please share writeup for data scientist for your experience. 
 
Please share resumes at Akhil@tror.ai 
 
Key Responsibilities 
 


* Lead end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, Llama, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems.
* Architect and implement Graph RAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding.
* Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval.
* Develop semantic retrieval systems with advanced document segmentation and indexing strategies.
* Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.
* Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals.
* Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments.
* Designing and implementing analytical frameworks.
* Developing predictive and prescriptive models. 
 
 

Preferred Qualifications 
 


* PhD or master’s degree in computer science, Machine Learning, or related field.
* 4+ years of experience in applied AI/ML, with a strong track record of delivering production-grade models. 
 
 

Deep Expertise In 
 


* LLM training and fine-tuning (e.g., GPT, Llama, Mistral, Qwen)
* Graph-based retrieval systems (Graph RAG, knowledge graphs)
* Embedding models (e.g., BGE, E5, SimCSE)
* Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
* Document segmentation and preprocessing (OCR, layout parsing)
* Distributed training frameworks (NCCL, Horovod, DeepSpeed)
* High-performance networking (InfiniBand, RDMA)
* Model fusion and ensemble techniques (stacking, boosting, gating)
* Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
* Symbolic AI and rule-based systems
* Meta-learning and Mixture of Experts architectures
* Reinforcement learning (e.g., RLHF, PPO, DPO)
* Experience applying causal inference techniques (e.g., causal impact analysis, uplift modeling, DoWhy) to marketing and engagement analytics.
* Exercise independent judgment in methods, techniques, and evaluation criteria on data science projects, overseeing the end-to-end process from problem definition to model implementation.
* Proficiency with programming languages like Python, R, and SQL.
* Strong background in predictive modeling, classification, segmentation, and optimization.
* Extensively worked in any Cloud environment. 
 
 

Bonus Skills 
 


* Familiarity with regulatory and compliance frameworks in AI deployment.
* Contributions to open-source AI projects or published research. And/Or ability to take research papers to poc – production.

Data

Project management

Machine learning

Database

Algorithm

Python

Data Science

Research

Cloud

Layout Design

Compliance

Network

Analysis

Marketing strategies

Data analytics

Graphs

Semantic Web

Reinforcement Learning

Predictive modeling

Classification

User segmentation

Code optimization

Research Papers

Data Scientist (In Person Interview)