Machine Learning Engineer

We are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.

The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).

You will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.

Key Responsibilities

Core ML/LLM Engineering

• Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.

• Implement RAG pipelines using vector databases.

• Work with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.

• Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.

• Integrate memory, evidence packs, and explainability modules into agentic pipelines.

• Work hands-on with multiple LLM ecosystems:

• OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).

• Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).

• Google Gemini (multimodal reasoning, advanced RAG integration).

• Meta LLaMA (fine-tuned/custom models for domain-specific tasks).

Data & Infrastructure

• Collaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.
• Conduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.
• Implement model monitoring, drift detection, and retraining pipelines.
• Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.

Research & Applied Innovation

• Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.
• Experiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).
• Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.
• Translate research prototypes into production-ready components.

Collaboration & Delivery

• Work cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.
• Participate in design reviews, architecture discussions, and model evaluations.
• Document processes, experiments, and results effectively for knowledge sharing.
• Mentor junior engineers and contribute to ML engineering best practices.

Skills, Knowledge and Expertise

Required

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.

• 3+ years of experience building and deploying ML systems.

• Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.

• Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).

• Demonstrated experience with at least two of the following ecosystems:

• OpenAI GPT models (chat, assistants, fine-tuning).

• Anthropic Claude (safety-first AI for reasoning and summarization).

• Google Gemini (multimodal reasoning, enterprise-scale APIs).

• Meta LLaMA (open-source, fine-tuned models).

• Familiarity with vector databases, embeddings, and RAG pipelines.

• Ability to work with structured and unstructured data at scale.

• Knowledge of SQL and distributed data frameworks (Spark, Ray).

• Strong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.

Preferred Qualifications

• Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
• Knowledge of AI safety, guardrails, and explainability techniques.
• Hands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
• Experience with CI/CD for ML (MLOps), monitoring, and observability.
• Familiarity with anomaly detection, fraud/risk modeling, or behavioral analytics.
• Contributions to open-source AI/ML projects or publications in applied ML research.

Seeking following AFSC/MOSs

US Army: 17D – Cyber Capability Developer 17C – Cyber Operations Specialist (Advanced Track) 35Q – Cryptologic Network Warfare Specialist 35N / 35P / 35S (Intel Analysts w/ coding exposure)

US AirForce: 17X – Cyberspace Warfare Operations 1B4X1 – Cyber Warfare Operations 9S100 – Scientific Applications Specialist 3D0X4 / 1D7X1 (Software / Data Ops variants)

US Navy: CTN – Cryptologic Technician (Networks) CTI / CTR (with analytics focus) Information Warfare Officers (1810)

US Marine Corps: 1721 – Cyberspace Warfare Operator 26XX Intel (with data/automation focus)

US Space Force: Cyber Operations (DCO/OCO) Guardians