Senior AI-ML Data Scientist

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.