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Senior AI-ML Data Scientist
Strategic Systems International. 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 .
Tech Stack
Tools & technologiesAWSAzureGoogle Cloud PlatformPythonPyTorchRaySQLTensorflow
About the role
Key responsibilities & impact- 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
- Design experiments, interpret model implementations, and ship production solutions within latency budgets
- Determine what agent memory should retain, in what form, and how retention is grounded in a governed data warehouse rather than an undifferentiated vector blob
- Partner closely with the AI Data Engineer, who owns the warehouse, pipelines, and index infrastructure
- Own the algorithm, prompt, and evaluation while collaborating on pipeline, schema, and data guarantees
Requirements
What you’ll need- 5–10+ years in ML/AI engineering, data science, or related technical roles
- Proven experience deploying models at scale in production (LLM, CV, NLP, or multimodal)
- Substantive command of machine learning algorithms and neural network theory, including optimization, regularization, attention mechanisms, tokenization, embeddings, and model internals
- Rigorous grounding in inference, experimental design, and data analysis
- PyTorch; TensorFlow or JAX; Hugging Face ecosystem (Transformers, Datasets, TRL)
- Expert-level, production-grade Python
- Strong SQL for analysis against a dimensional warehouse
- Production experience with LangChain/LangGraph or equivalent and a well-considered position on agent memory architecture
- Hands-on ontology design and graph-based reasoning
- 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 and Weights & Biases
- Preferred: direct experience implementing CoALA or comparable cognitive architecture (SOAR, ACT-R, or documented in-house framework) in a shipped agent system
- Preferred: GPU acceleration internals including CUDA, TensorRT, and cuBLAS
- Preferred: production experience with vLLM, NVIDIA Triton, Ray Serve/Ray Train, DeepSpeed, or FSDP
- Preferred: experience with AI security, governance, and compliance frameworks
- Preferred: track record contributing to open-source AI frameworks or published research
- Preferred: ability to lead technical discovery phases and client-facing AI workshops
- Preferred: familiarity with lakehouse table formats such as Iceberg and Delta Lake