Senior AI-ML Data Scientist
At a glance
Senior AI Research role at Strategic Systems International. Lahore · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Frame ML problems, select algorithms, fine-tune models, and take them through production serving
- Design retrieval, agent orchestration, and memory grounded in a governed warehouse
- Evaluate model behavior through experiments, statistical analysis, and production metrics
- Work with the AI data engineer to define the boundary between algorithms and evaluation versus pipelines and schemas
What you bring
- At least five years in ML, AI engineering, data science, or a related technical role, with models deployed at production scale
- Strong grounding in ML theory, neural networks, statistics, experimental design, and data analysis
- Expert Python skills, strong SQL, and experience with PyTorch plus TensorFlow or JAX
- Production experience with agent systems and a considered approach to agent memory design
- Hands-on knowledge graph work, plus cloud deployment expertise across AWS, Azure, or GCP
Who this fits
This is a senior, hands-on role for someone who can move from ML theory and experiment design to production systems. You’ll own algorithms, prompts, and evaluation while collaborating with an AI data engineer responsible for warehouse and pipeline infrastructure.
From the employer
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.