Senior AI-ML Data Scientist at Strategic Systems International

Job description

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.

Requirements

Must-haves

Senior AI-ML Data Scientist
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).

Nice-to-haves

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.

Application prep

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Keywords to mirror

aimlmachine learningllmpythonsqlawsazuregcpci/cd

Platform: breezy
Effort: medium

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