Job VC
Data Scientist/ML Engineer
Technologies
Description
Part-time, 3-4 hours a day
No need for the US time overlap, but should be available for meeting from 9 AM – 12 PM ET
On behalf of our Client from USA, Mobilunity is looking for a Data Scientist
We are looking for a Senior Data Scientist with expertise in Large Language Models (LLMs) and
Generative AI to lead the design and delivery of end-to-end AI systems that generate
high-quality insights from complex unstructured datasets. This role requires strong ownership
across the full lifecycle from data preparation and model fine-tuning to RAG pipeline architecture
and scalable deployment on AWS.
Key Responsibilities:
Architect and implement end-to-end LLM-based solutions for insight generation and
automation
Lead the design and optimization of Retrieval-Augmented Generation (RAG) pipelines,
including ingestion, chunking strategies, embedding generation, indexing, and retrieval tuning
Fine-tune, evaluate, and optimize LLMs using advanced techniques (e.g., instruction tuning,
LoRA)
Define and enforce best practices for data preprocessing, cleaning, normalization, and
transformation across diverse data sources
Provide hands-on guidance and code/data reviews for existing data scientists
Help the team develop practical intuition for LLM fine-tuning and evaluation
Establish simple, repeatable workflows for experimentation and iteration
Prevent over-engineering and ensure focus on business outcomes
Design scalable and cost-efficient AI/ML solutions leveraging AWS services
Own the development of vector search infrastructure and embedding pipelines
Establish robust evaluation frameworks for LLM outputs (accuracy, relevance, hallucination reduction)
Collaborate cross-functionally with engineering, product, and business stakeholders to
translate requirements into AI solutions
Drive MLOps best practices including versioning, monitoring, CI/CD, and performance
optimization
Ensure data governance, security, and compliance in all AI workflows
Required Skills & Qualifications:
Master’s or PhD in Computer Science, Data Science, Machine Learning, or related field (or equivalent experience)
3+ years of experience in data science, machine learning, or applied AI roles
Experience mentoring or leading data scientists in applied ML/LLM projects
Proven experience designing and deploying LLM-based solutions in production
Hands-on experience fine-tuning open-source LLMs (Llama, Mistral, Qwen)
Experience with LoRA / QLoRA approaches
Ability to work with limited datasets and iterate quickly
Strong understanding of trade-offs between model size, cost, and performance
Strong expertise in RAG architectures and semantic search systems
Deep understanding of NLP, embeddings, and transformer-based models
Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow)
Hands-on experience with vector databases (e.g., FAISS, Pinecone, OpenSearch, Weaviate)
Strong experience with AWS ecosystem (e.g., S3, SageMaker, Bedrock, Lambda, EC2, Step
Functions)
Experience building scalable data pipelines and distributed systems
Solid understanding of API design, microservices, and system integration
Mandatory skills:
real production experience with LLM
RAG pipelines (end-to-end)
fine-tuning (LoRA / QLoRA)
vector DB (FAISS / Pinecone / etc.)
AWS (S3, SageMaker, etc.)
Preferred Qualifications:
Experience with RLHF, prompt optimization, and evaluation frameworks for LLMs
Experience with real-time or streaming data pipelines
Knowledge of cost optimization strategies for LLM workloads
English: B2
No need for the US time overlap, but should be available for meeting from 9 AM – 12 PM ET
On behalf of our Client from USA, Mobilunity is looking for a Data Scientist
We are looking for a Senior Data Scientist with expertise in Large Language Models (LLMs) and
Generative AI to lead the design and delivery of end-to-end AI systems that generate
high-quality insights from complex unstructured datasets. This role requires strong ownership
across the full lifecycle from data preparation and model fine-tuning to RAG pipeline architecture
and scalable deployment on AWS.
Key Responsibilities:
Architect and implement end-to-end LLM-based solutions for insight generation and
automation
Lead the design and optimization of Retrieval-Augmented Generation (RAG) pipelines,
including ingestion, chunking strategies, embedding generation, indexing, and retrieval tuning
Fine-tune, evaluate, and optimize LLMs using advanced techniques (e.g., instruction tuning,
LoRA)
Define and enforce best practices for data preprocessing, cleaning, normalization, and
transformation across diverse data sources
Provide hands-on guidance and code/data reviews for existing data scientists
Help the team develop practical intuition for LLM fine-tuning and evaluation
Establish simple, repeatable workflows for experimentation and iteration
Prevent over-engineering and ensure focus on business outcomes
Design scalable and cost-efficient AI/ML solutions leveraging AWS services
Own the development of vector search infrastructure and embedding pipelines
Establish robust evaluation frameworks for LLM outputs (accuracy, relevance, hallucination reduction)
Collaborate cross-functionally with engineering, product, and business stakeholders to
translate requirements into AI solutions
Drive MLOps best practices including versioning, monitoring, CI/CD, and performance
optimization
Ensure data governance, security, and compliance in all AI workflows
Required Skills & Qualifications:
Master’s or PhD in Computer Science, Data Science, Machine Learning, or related field (or equivalent experience)
3+ years of experience in data science, machine learning, or applied AI roles
Experience mentoring or leading data scientists in applied ML/LLM projects
Proven experience designing and deploying LLM-based solutions in production
Hands-on experience fine-tuning open-source LLMs (Llama, Mistral, Qwen)
Experience with LoRA / QLoRA approaches
Ability to work with limited datasets and iterate quickly
Strong understanding of trade-offs between model size, cost, and performance
Strong expertise in RAG architectures and semantic search systems
Deep understanding of NLP, embeddings, and transformer-based models
Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow)
Hands-on experience with vector databases (e.g., FAISS, Pinecone, OpenSearch, Weaviate)
Strong experience with AWS ecosystem (e.g., S3, SageMaker, Bedrock, Lambda, EC2, Step
Functions)
Experience building scalable data pipelines and distributed systems
Solid understanding of API design, microservices, and system integration
Mandatory skills:
real production experience with LLM
RAG pipelines (end-to-end)
fine-tuning (LoRA / QLoRA)
vector DB (FAISS / Pinecone / etc.)
AWS (S3, SageMaker, etc.)
Preferred Qualifications:
Experience with RLHF, prompt optimization, and evaluation frameworks for LLMs
Experience with real-time or streaming data pipelines
Knowledge of cost optimization strategies for LLM workloads
English: B2