Data Scientist

About Archer

A new regulatory change lands somewhere in the world every six minutes, and agentic AI is outpacing most teams' ability to govern it. At Archer, we help the enterprises powering the global economy turn that pressure into an advantage.

Mandates like the EU AI Act and the SEC cybersecurity disclosure rules are driving the most significant GRC buying cycle we have seen in years. That demand is why we are growing this team.

This is hard work that matters. Our customers span energy, utilities, financial services, pharmaceuticals, and the public institutions communities depend on. People choose Archer because they get to partner with the most trusted brands in the world, on work that protects the customers and communities those brands serve.

If that sounds like your kind of work, let's chat.

The Role

We are seeking an experienced Data Scientist with a strong background in AI model integration, data pipeline development, and knowledge base (KB) engineering to support our next-generation LegalTech / RegTech AI platform.

This role blends applied machine learning, data engineering, and software development, focusing on building scalable pipelines that connect large language models (LLMs) to structured and unstructured data through retrieval-augmented generation (RAG) and vector database architectures.

The ideal candidate is passionate about operationalizing AI — from training and fine-tuning models to deploying intelligent retrieval systems in AWS cloud environments.

What You'll Do

1. AI Model Integration & Development

• 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.

1.5 Agentic and MCP Knowledge Integration:

• 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.

2. Data Pipeline Engineering

• 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.

3. Knowledge Base & Information Retrieval

• 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.

4. AWS & Deployment

• 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.

5. Cross-Functional Collaboration

• 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.

You'll Thrive Here If You Have

• 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

Even Better If You Have

• 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.

Soft Skills

• 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.

Success Indicators

• Scalable and well-documented RAG pipelines supporting production of AI workloads.
• High model accuracy, retrievability, and latency efficiency.
• Reliable data flow from ingestion to inference with minimal manual intervention.
• Increased explainability and compliance assurance across AI outputs.

Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice at management discretion based on business need.

Equal Opportunity

Archer is committed to the principle of equal employment opportunity for all employees and applicants for employment and to providing employees with a work environment free of discrimination and harassment. All employment decisions at Archer are based on business needs, job requirements and individual qualifications, without regard to race, color, religion, national origin, sex (including pregnancy), age, disability, sexual orientation, gender identity and/or expression, marital, civil union or domestic partnership status, protected veteran status, genetic information, or any other characteristic protected by federal, state or local laws. Archer will not tolerate discrimination or harassment based on any of these characteristics. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. All Archer employees are expected to support this policy and contribute to an environment of equal opportunity.

If you need a reasonable accommodation during the application process, please contact [email protected]. All employees must be legally authorized to work in Country they are applying for. Archer and its approved consultants will never ask you for a fee to process or consider your application for a career with Archer. Archer reserves the right to amend or withdraw any job posting at any time, including prior to the advertised closing date.

Pay Transparency Notice: We’re committed to fair and transparent pay practices. In line with state pay transparency laws, for positions in this location, we offer a base pay of $129,200.00 to $215,400.00 USD, plus benefits. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.

Please contact our Talent Acquisition team at [email protected] for the range and related compensation details. Actual pay may vary based on location, experience, skills, and internal equity.