AIML Data Scientist

About LexisNexis LexisNexis Legal & Professional serves customers in over 150 countries with trusted legal regulatory and business information. We are transforming the legal industry through cutting-edge AI scalable data platforms and intelligent research systems that power high-stakes decision-making for legal professionals worldwide. Our Global AI Platform Team builds the foundational infrastructure behind next-generation AI products including LLM-powered research assistants retrieval systems and enterprise-grade agentic Opportunity We are seeking a Sr. Machine Learning Engineer to define and lead the architecture of scalable AI/ML and agentic systems across our global product portfolio. This is a senior technical leadership role for someone who thrives at the intersection of:

• Large-scale distributed ML systems
• LLM and RAG architectures
• Agentic AI frameworks and tool orchestration
• Enterprise platform engineering

You will shape the long-term AI platform strategy and establish technical standards that impact millions of users. What Youll Do Architect Scalable AI Platforms

• Define reference architecture for LLM ML and agent-based systems across products.
• Design high-availability low-latency inference platforms for global scale.
• Establish reusable platform components for model lifecycle deployment and monitoring.

Lead Agentic AI & Tool Ecosystems

• Architect multi-step reasoning-driven agent systems.
• Design orchestration patterns for tool use API invocation and structured function calling.
• Lead implementation and governance of Model Context Protocol (MCP) servers to standardize tool integration and context management.
• Define guardrails permissions and audit mechanisms for enterprise-safe AI systems.

Elevate Engineering Standards

• Set best practices for MLOps CI/CD observability and system reliability.
• Embed Responsible AI principles across platform architecture.
• Mentor senior engineers and influence technical direction across teams.

What Were Looking For Experience

• Education requirement: 10 yrs of experience with Masters degree or 12 yrs of experience with bachelor degree
• 10 years building production-grade ML systems at scale.
• Extensive experience with LLMs generative AI and RAG systems in real-world deployments.
• Proven expertise designing distributed systems in cloud environments (AWS Azure or GCP).
• Hands-on experience with Kubernetes containerization and scalable inference systems.
• Experience designing agentic systems and tool orchestration frameworks.
• Experience implementing or governing MCP servers or structured tool-calling architectures.

Technical Strength

• Strong Python engineering background.
• Experience with vector databases and search systems.
• Deep understanding of model evaluation reliability and monitoring.
• Strong architectural judgment and systems thinking.

Leadership

• Demonstrated ability to influence technical direction across teams.
• Strong communication skills and executive presence.
• Experience mentoring senior engineers or leading cross-functional initiatives.

Work with us in the worlds leading firm of AI and in the professional domain.

Job Responsibilities

Work in with talents across the world in a globally leading firm.

State-of-the-art Agentic AI development in a leading professional legal AI firm

Implementing AI security cryptography and state-of-the-art homomorphic cryptography

Being able to understand from the big picture to detailed system implementation of big data and AI retrieval architecture supporting and innovating as a global platform.

Be able to manoeuvre in a very dynamic environment requires effective communication and social skills.

Being able to bridge and communicate across functional teams locally and globally.

Documentation drafting including architecture solution comparisons etc.

Have to be dynamically and highly skilled at conducting Proof of Concept systems design in a wide variety of functional domains.

To conduct various testing for selection of proper libraries frameworks

To design and implement testing frameworks as guardrails for code delivery quality and behavioral tests.

Attending meetings with global teams to liaise and to provide support or systems diagnosis.

Rapid learner

Communication with key stakeholders and presenting industry-leading solutions in a professional manner

Skills Required

Comprehensive knowledge in the systems domain

Knowledge of Agentic AI and RAG architecture is high advantageous

Preferably: Go Rust Python C Java

Hands on Linux/Unix skills

Hands on skills on cloud architecture (AWS Azure GCP)

Robust fundamentals in data architecture and infrastructures particularly NoSQL (Not only RDBMS but columnar databases vector databases K/V databases search engines Aerospike Clickhouse ElasticSearch Solr ELK Cassandra HBase Cloud-based Spanner Aurora DynamoDB etc.)

Solid knowledge in message-driven/event-driven architectures patterns (Kafka RabbitMQ ActiveMQ SQS EventBridge etc.)

Solid knowledge in microservice architectures (CQRS Saga Composition Replica etc.)

Solid foundation in stringent data security solutions

DevSecOps skills (CI CD Jenkins Kubernetes Karpenter CloudFormation Terraform IaC EKS GKE SonarQube etc.)

Cloud networking design (L4/L7 load balancers and limitations DR methodology rate limiting performance sizing binpacking cloud Kubernetes ingress types Istio etc.)

Systems and code security (Hashing deprecation and vulnerabilities FIPS compliance etc.)

Hands on coding skills with strong fundamentals of computer science data structures software and computer architecture (at least 3 languages in previous projects)

Knowledge in performance web frameworks for microservices architecture (e.g. Fiber Gin Spring Boot FastAPI Enterprise J2EE)

Knowledge in web serving technologies (e.g. Jetty Tomcat Uvicorn Guvicorn nginx Apache HTTPD)

Knowledge and exposure in ETL architectures and implementations (Flink Spark streaming)

Strong fundamentals in maintaining clean code styling best practices algorithmic performance performance and crash-free robust software architectures.

Knowledge in end-to-end observability solutions (e.g. OpenTelemetry Jaeger Splunk Coralogix structured metrics Prometheus Grafana Datadog ELK DTrace)

Possession of cloud certification is highly advantageous

- Education: Masters 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.

TEKsystems is partnered with a software company in Raleigh that needs to hire a Senior Data Scientist for their flagship product LexisNexis Legal & Professional a leading global provider of information and analytics. Recently LexisNexis has focused on the general availability of Lexis AI for U.S. customers a generative AI solution designed to transform legal work. Lexis AI delivers trusted results in a familiar easy-to-use interface with linked hallucination-free legal citations that combine the power of generative AI with proprietary LexisNexis search technology Shepards Citations functionality and authoritative content.

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/LlamaIndex 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.