Senior AI-ML Data Scientist
Job Summary
We are seeking a Senior AI/ML Data Scientist to own model and agent behavior end to end - from problem framing and algorithm selection through fine-tuning, retrieval design, agentic orchestration, evaluation, and production serving.
This is a hands-on role for someone with genuine depth in machine learning and statistics who is equally comfortable designing an experiment, reading an attention implementation, and shipping the result behind a latency budget. We are particularly interested in candidates who think carefully about agent memory - what an agent should retain, in what form, and how retention is grounded in a governed data warehouse rather than an undifferentiated vector blob.
This role partners closely with the AI Data Engineer, who owns the warehouse, pipelines, and index infrastructure. The boundary: they own the pipeline, the schema, and the guarantees; you own the algorithm, the prompt, and the evaluation.
Required Qualifications
5–10+ years in ML/AI engineering, data science, or related technical roles, with proven experience deploying models at scale in production (LLM, CV, NLP, or multimodal).
ML depth: substantive command of machine learning algorithms and neural network theory -optimization, regularization, attention mechanisms, tokenization, embeddings, and model internals.
Statistics: rigorous grounding in inference, experimental design, and data analysis.
Frameworks: PyTorch (primary), plus TensorFlow or JAX; the Hugging Face ecosystem (Transformers, Datasets, TRL).
Python: expert-level, production-grade. Strong SQL for analysis against a dimensional warehouse.
Agentic systems: production experience with LangChain/LangGraph or equivalent, and a well considered position on agent memory architecture.
Knowledge graphs: hands-on ontology design and graph-based reasoning.
Cloud: expert-level deployment of AI workloads on AWS, Azure, or GCP, including GPU provisioning, cost optimization, containerization, and CI/CD.
Experience with experiment tracking and model lifecycle tooling (MLflow, Weights & Biases).
Preferred Qualifications
Direct experience implementing CoALA or a comparable cognitive architecture (SOAR, ACT R, or a documented in-house framework) in a shipped agent system.
GPU acceleration internals: CUDA, TensorRT, cuBLAS.
Production experience with vLLM, NVIDIA Triton, Ray Serve/Ray Train, DeepSpeed, or FSDP.
Experience with AI security, governance, and compliance frameworks.
Track record of contributing to open-source AI frameworks, or published research.
Ability to lead technical discovery phases and client-facing AI workshops.
Familiarity with lakehouse table formats (Iceberg, Delta Lake) sufficient to collaborate credibly with data engineering.
Skills Required
- 5-10+ years of experience in ML/AI engineering, data science, or related technical roles
- Proven experience deploying machine learning models at scale in production, including LLM, computer vision, NLP, or multimodal models
- Substantive command of machine learning algorithms and neural network theory, including optimization, regularization, attention mechanisms, tokenization, embeddings, and model internals
- Rigorous grounding in statistical inference, experimental design, and data analysis
- Experience with PyTorch, TensorFlow or JAX, and the Hugging Face Transformers, Datasets, and TRL ecosystem
- Expert-level production Python and strong SQL for dimensional warehouse analysis
- Production experience with LangChain, LangGraph, or equivalent agentic systems, including agent memory architecture
- Hands-on ontology design and graph-based reasoning using knowledge graphs
- Expert-level deployment of AI workloads on AWS, Azure, or GCP, including GPU provisioning, cost optimization, containerization, and CI/CD
- Experience with experiment tracking and model lifecycle tooling such as MLflow or Weights & Biases
- Direct experience implementing CoALA or a comparable cognitive architecture in a shipped agent system
- Experience with GPU acceleration internals including CUDA, TensorRT, or cuBLAS
- Production experience with vLLM, NVIDIA Triton, Ray Serve, Ray Train, DeepSpeed, or FSDP
- Experience with AI security, governance, and compliance frameworks
- Track record contributing to open-source AI frameworks or publishing research
- Ability to lead technical discovery phases and client-facing AI workshops
- Familiarity with Iceberg or Delta Lake sufficient for collaboration with data engineering
What We Do
Strategic Systems International (SSI) is a fast-growing Advanced Analytics and Software Engineering firm that partners with tech companies to help them launch and scale their products. The company was launched in 1991 by alumni of University of Chicago and Northwestern has grown to 200 employees with presence in US, Europe and Asia. We architect a
