Principal Machine Learning Engineer

Role : Principal Machine Learning Engineer

Duration : 6 12 Month Contract to Hire

Location : Remote

Responsibilities will include:

• Developing proofs of concept and initial implementations
• Evaluating and test-driving new frameworks and tools
• Working closely with the architect
• Providing technical strategy and keeping current with industry standards
• Understanding the full environment and systems end to end

Candidates for the Principal role should have played a senior role in designing, architecting and implementing a new agentic RAG-based system. They do not want someone who was simply one contributor within a large team or whose resume only mentions increasing throughput, reducing costs or improving efficiency. They want evidence that the person helped establish the system and made meaningful architectural or technical decisions.

Requirements:

Deep expertise in LLMs, RAG, agentic systems, MCP, vector databases, cloud platforms (AWS/Azure/GCP), and data engineering, combined with strong skills in Python, AI architecture, MLOps, and software engineering best practices. Plus, GoLang would be beneficial.

Summary:

• Client is seeking a Principal Machine Learning Engineer to provide technical strategy and architecture leadership across Machine Learning, AI, and Generative AI initiatives within the legal domain. This is a deeply technical individual-contributor leadership role for an engineer who began their career in software development and later transitioned into ML/AI.
• The Principal ML Engineer will define technical direction, evaluate legacy systems, lead proof-of-concept initiatives, and guide long-term platform decisions around LLMs, RAG, hybrid retrieval, and agentic AI systems. The role is not focused on day-to-day coding or people management; instead, it requires exceptional technical depth, architectural judgment, and the ability to translate complex technical concepts into clear recommendations for technical and non-technical stakeholders.

Responsibilities:

• Serve as the principal technical authority for Machine Learning and GenAI initiatives.
• Define and communicate technical roadmaps, architectural direction, and long-term AI strategy.
• Evaluate legacy systems and platforms, identifying technical constraints, risks, dependencies, and modernization opportunities.
• Make high-impact technical decisions that guide engineering teams and influence platform evolution.
• Lead and design POCs to validate new ML, GenAI, tooling, frameworks, and architectural approaches.
• Assess feasibility, scalability, integration requirements, and operational considerations before broader adoption.
• Translate experimental results into clear architectural recommendations and implementation direction.
• Understand complex backend systems, data flows, APIs, microservices, integrations, and legacy codebases.
• Bridge historical system architecture with future-state ML and AI capabilities.
• Partner closely with engineering and architecture teams to ensure strategic decisions align with implementation realities.
• Guide the design and delivery of LLM, RAG, hybrid retrieval, and agentic workflow patterns.
• Define experimentation and evaluation strategies for AI systems.
• Establish quality metrics and monitoring approaches that identify degradation in AI system performance.
• Determine how AI results should be measured, improved, and continuously tuned.
• Guide decisions around dataset chunking, embedding strategies, pre-filtering, keyword augmentation, semantic search, hybrid retrieval, reranking, and intelligence thresholds.
• Help establish golden datasets and evaluation approaches that define target system performance.
• Evaluate agentic systems and determine appropriate quality metrics and supervision mechanisms.
• Maintain awareness of emerging agentic frameworks and determine where new approaches can provide architectural value.
• Provide technical leadership across engineering, product, platform, and architecture teams.

Qualifications:

• 10+ years of overall technology experience.
• Early-career experience as a Software Engineer / Developer.
• 5+ years of experience in Machine Learning, Artificial Intelligence, or Generative AI.
• Strong foundation in Java and/or Python, including the ability to read, understand, and assess legacy code.
• Experience with or willingness to learn Go and other modern software languages.
• Advanced experience with ML, AI, or GenAI at the architectural and system-design level.
• Strong understanding of backend engineering, microservices, APIs, system design, and data-driven architectures.
• Strong cloud computing and distributed-systems knowledge.
• Experience with LLMs, RAG, hybrid retrieval, and agentic workflows.
• Strong understanding of experimentation, evaluation, benchmarking, and production monitoring for AI systems.
• Ability to make technical decisions at scale without direct ownership of day-to-day coding.
• Demonstrated technical leadership without direct people-management responsibilities.
• Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.

Preferred Qualifications:

• Strong Python and Java experience with working familiarity in Go, shell scripting, Git/GitHub, Docker, Kubernetes, and Helm.
• AWS experience, particularly EKS, cloud deployment patterns, and GitOps workflows.
• Experience with CI/CD and modern software delivery practices.
• Experience designing production-oriented RAG and agentic AI systems.
• Experience with vector/embedding strategies, hybrid search, reranking, semantic search, and retrieval evaluation.
• Experience developing golden datasets and evaluation frameworks for AI systems.
• Experience determining quality metrics for monitoring AI/agentic system degradation.
• Experience working with complex legacy modernization initiatives.