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Job Title: Data scientist

Location: Remote

This role leads the design and development of an advanced multi‑agent AI platform that powers intelligent research, drafting, and reasoning capabilities for large‑scale enterprise knowledge environments. You will architect agent frameworks, optimize retrieval‑augmented generation pipelines, fine‑tune language models, and build the infrastructure that enables AI systems to collaborate, plan, and execute complex tasks reliably. The work directly shapes the next generation of AI‑driven professional tools used by experts in high‑stakes domains.

Core Responsibilities

Architect and implement multi‑agent systems capable of planning, tool use, and coordinated task execution.
Design and optimize RAG pipelines including embeddings, hybrid retrieval, reranking, and context‑window strategies.
Fine‑tune and evaluate small, medium, and large language models for domain‑specific reasoning and summarization.
Develop prompt engineering frameworks, guardrails, and automated evaluation suites for agent reliability.
Build scalable ML services and APIs for production deployment in distributed environments.
Collaborate with product, engineering, and domain experts to translate complex workflows into agentic AI solutions.
Establish best practices for model evaluation, observability, safety, and compliance.
Mentor DS/ML engineers and contribute to long‑term AI strategy and architecture.

Required Expertise

6–12+ years in Data Science / ML Engineering, with deep experience in LLM‑based systems.
Proven experience building agentic architectures (planner‑executor, tool‑use agents, ReAct‑style reasoning).
Strong background in RAG, embeddings, retrieval optimization, and evaluation.
Expertise in NLP, transformers, deep learning, and model fine‑tuning.
Proficiency with PyTorch, HuggingFace, LangChain/Llama Index, Ray, Kubernetes, and vector databases.
Experience designing production‑grade ML systems with monitoring, evaluation, and observability.
Strong communication skills and ability to lead technical direction.

Preferred Qualifications

Experience in enterprise search, knowledge management, or high‑compliance domains.
Experience with model distillation, LoRA/QLoRA, PEFT, and model compression.
Experience building evaluation frameworks for hallucination, grounding, and agent reliability.
Familiarity with knowledge graphs, symbolic reasoning, or hybrid neuro‑symbolic systems.
Publications, patents, or open‑source contributions in LLMs or agent systems.
Strong coding skills in Python 7+ years
Be a natural problem solver, able to take a lead in collaborating to resolve issues
Proficiency in IDE debugging : VSCODE and PYCHARM
Have communication skills
5+ years of experience in AI and machine learning
Deep understanding of machine learning algorithms, classification models, diagnostic testing of models
Experience working directly and Transformer based architectures including BERT, RoBERTa, T5 etc. Nd familiarity with large language models and fine tuning
Experience with conversational search / semantic search, reinforcement learning, prompt engineering, hallucination mitigation
Working understanding of the business risks associated with applying LLM (LangChain) in a business
Experience working with AWS, RAG, SageMaker, SQL

Enterprise Req Skills

data science, nlp, indexing, semantic search, conversational search, LangChain, Generative AI, Large language model, Sql, aws, algorithm, artificial intelligence, Retrieval augmented generation, fine-tuning, Python, Machine learning, cloud computing

Top Skills Details

PLEASE REVIEW IN DETAILS AS CANDIDATES WILL NEED TO HAVE THE FOLLOWING SKILLS (NO EXCEPTIONS):

Education: Master\''s or PhD Preferred
6–12+ years in Data Science / ML Engineering, with deep experience in LLM‑based systems.
Proven experience building multi-agent architectures (planner‑executor, tool‑use agents, ReAct‑style reasoning).
Strong background in RAG, embeddings, retrieval optimization, and evaluation.
Expertise in NLP, transformers, deep learning, and model fine‑tuning.
Proficiency with PyTorch, HuggingFace, LangChain/LlamaIndex, RAG, Kubernetes, and vector databases.
Experience designing production‑grade ML systems with monitoring, evaluation, and observability.

Nice to haves: