Note: The job is a remote job and is open to candidates in USA. Archer Integrated Risk Management is a leading provider of integrated risk management solutions focused on enhancing decision-making and operational resilience. They are seeking an experienced Data Scientist to support their LegalTech/RegTech AI platform by developing AI model integrations, data pipelines, and knowledge bases.
Responsibilities
- Design, train, and evaluate LLM-based pipelines for document understanding, obligation extraction, and regulatory reasoning
- Implement and optimize RAG architectures, combining LLMs with vector databases for semantic retrieval
- Develop and maintain model fine-tuning workflows, embedding generation, and knowledge distillation
- Collaborate with ML Ops teams to integrate AI models into production-ready APIs and services on AWS
- Measure and improve model precision, recall, latency, and interpretability
- Design and maintain agentic multi-component processes (MCPs) that enable context-aware reasoning across multiple data sources and agents
- Implement AI agents capable of dynamic tool use, autonomous task decomposition, and multi-context knowledge retrieval
- Develop pipelines that support agent memory, self-reflection, and knowledge synthesis across distributed systems and knowledge bases
- Collaborate with engineering teams to integrate MCP-driven agents with retrieval, analytics, and workflow orchestration layers, ensuring compliance with regulatory reasoning frameworks
- Build and manage end-to-end data pipelines for ingestion, transformation, embedding, and indexing of legal and compliance data
- Orchestrate data workflows leveraging AWS services (e.g., S3, Lambda, Glue, SageMaker, Step Functions, RDS)
- Develop scalable ETL/ELT processes to feed both relational (PostgreSQL) and vector databases (e.g., Pinecone, FAISS, Weaviate, Elastic Vector Search)
- Ensure data lineage, reproducibility, and version control across AI and analytics pipelines
- Automate retraining and evaluation pipelines for continuous learning from user feedback
- Architect and maintain intelligent Knowledge Bases (KBs) to support AI-driven search, summarization, and compliance reasoning
- Implement advanced retrieval techniques using ElasticSearch / Elastic Vector Search and embedding-based retrieval
- Align KB structures with business ontologies and regulatory taxonomies to support explainable AI outputs
- Collaborate with domain experts and PMs to enrich KB metadata and enhance model context relevance
- Deploy and scale AI pipelines using AWS services such as SageMaker, Lambda, ECS/EKS, API Gateway, and CloudFormation/Terraform
- Implement model and data monitoring solutions for drift detection, latency management, and cost optimization
- Collaborate with DevOps to maintain secure, reliable, and compliant cloud environments
- Partner with engineering, product, and compliance teams to align AI models with regulatory and data governance requirements
- Work closely with QA and Professional Services teams to validate AI outputs and improve client-facing performance
- Document architectures, experiment results, and data flows to ensure transparency and reproducibility
Skills
- 5+ years of experience in data science, ML engineering, or AI-driven software development
- Strong programming skills in Python (NumPy, Pandas, PyTorch/TensorFlow, LangChain, or equivalent)
- Experience with vector databases and retrieval systems (Pinecone, FAISS, Weaviate, Qdrant, or Elastic Vector Search)
- Hands-on experience with RAG pipelines, embedding models, and LLM orchestration (OpenAI, Bedrock, Hugging Face, etc.)
- Solid understanding of data pipelines, ETL frameworks, and cloud-native deployment on AWS
- Familiarity with Elasticsearch, PostgreSQL, and API integration patterns
- Knowledge of ML lifecycle management, including model training, evaluation, and monitoring
- Strong problem-solving and system design capabilities
- Excellent communication skills for cross-disciplinary collaboration
- Passion for structured documentation, reproducibility, and experimentation
- Adaptable mindset with focus on performance, scalability, and reliability
- Experience building AI products for LegalTech, RegTech, or compliance automation
- Familiarity with agentic AI frameworks (e.g., OpenAI MCP, CrewAI, LangGraph, or AutoGen)
- Background in document intelligence systems, multi-agent orchestration, or knowledge graph integration
- Experience with LangChain, LlamaIndex, or similar frameworks for RAG orchestration
- Hands-on knowledge of MLOps tools and data versioning (DVC, MLflow, Weights & Biases)
- Understanding of governance, interpretability, and ethical AI
Company Overview