Associate, AI Data Scientist- Private Markets

About this role

Build efficient, trusted agentic AI for private-markets intelligence

Research, build and scale advanced agentic systems and fit-for-purpose language models that turn complex information into dependable data, research and decision intelligence.

Your team

You will join a multidisciplinary AI and data science team focused on applying advanced AI to high-value challenges in private markets. The team brings together data science, engineering, product and domain expertise, and works closely with users to develop practical capabilities that improve how information is acquired, researched and managed.

You will work in an environment where applied research is expected to become dependable production capability. We value intellectual curiosity, rigorous experimentation, strong engineering and responsible AI, with quality, security and appropriate human oversight designed in from the start.

Your role & impact

As an Associate, AI Data Scientist, you will be a hands-on technical contributor responsible for researching, building and scaling enterprise-grade agentic AI capabilities. Working directly with users and senior technical leaders, you will translate complex private-markets workflows into well-evaluated systems that move from experimentation to governed production and sustained adoption.

Your core mandate is to advance multi-agent LLM systems through effective agent design patterns, rigorous evaluation and disciplined model engineering. You will develop workflows that plan, retrieve, reason, use tools, verify and escalate appropriately, while selecting the simplest architecture and model that can meet the required quality, latency, cost and control objectives.

You will also help establish the lifecycle for open-source small language models and LLMs, from model discovery and benchmarking through training, adaptation, fine-tuning, deployment, monitoring and retirement. Your impact will be visible in stronger data quality, faster research, scalable automation and reusable AI capabilities that help BlackRock Preqin remain at the forefront of private-markets data and technology.

Your responsibilities

· Work forward-deployed with researchers, data specialists and product teams to understand workflows, frame ambiguous problems, define measurable outcomes and iterate rapidly from evidence to production.

· Research, design and deliver agentic workflows spanning planning, retrieval, tool use, structured extraction, reasoning, verification, exception handling and human escalation, using single-agent or multi-agent patterns where each is demonstrably appropriate.

· Engineer advanced agent patterns, including bounded iterative loops, reflection and critique, planner-executor and evaluator-optimizer patterns, with explicit stop conditions, budgets and safeguards that prevent unproductive or uncontrolled execution.

· Build agent and model harnesses that standardise tool interfaces, context assembly, state and memory handling, policy enforcement, tracing, replay and failure recovery.

· Determine when an agentic architecture, retrieval workflow, specialised model or deterministic service is the best fit, and validate the decision against representative tasks and simpler baselines.

· Design safe caching and reuse across retrieval, prompts, model responses, tool results and intermediate agent state, with clear cache keys, invalidation, freshness, access-control and provenance requirements.

· Build systematic evaluation harnesses using representative datasets, explicit quality dimensions, adversarial and edge-case testing, regression suites, human feedback and monitored production outcomes.

· Research and benchmark open-source SLMs and LLMs for task fit, considering quality, inference performance, cost, licensing, security, deployment constraints and operational supportability; apply fit-for-purpose routing and batching where they improve efficiency.

· Develop model-adaptation strategies using prompt and context optimisation, supervised fine-tuning, parameter-efficient fine-tuning or other suitable techniques, supported by governed dataset creation, experiment tracking, reproducibility and controlled promotion criteria.

· Operate the lifecycle for trained and fine-tuned open-source models, including data and model versioning, lineage, experiment and artifact management, evaluation gates, registry and release controls, deployment, performance and drift monitoring, rollback, retraining triggers and retirement.

· Build production-quality Python components that integrate models with enterprise data, retrieval services, APIs, workflow orchestration and human-review experiences.

· Improve grounding and trust through attribution, provenance, uncertainty handling, validation checks, access controls and clear separation of source evidence from model-generated conclusions.

· Own delivered reliability across testing, observability, latency, cost and maintainability, and diagnose model, data, prompt and orchestration failures in production.

· Document findings, reusable patterns and trade-offs; contribute to design reviews; and communicate results clearly to research, product, engineering, risk and senior stakeholders.

· Partner with engineering, product, risk, privacy, information security, legal and compliance colleagues to support responsible, secure and accountable use of AI.

Required experience

We are looking for a hands-on applied AI practitioner who combines strong research and modelling skills with production judgment and user-focused problem solving. You should bring:

· Strong experience building and deploying AI or machine-learning systems in an enterprise, SaaS, data-product or similarly complex environment, with evidence of taking work beyond prototypes.

· Practical depth in LLM applications and agentic systems, including retrieval, tool use, planning, orchestration, memory or state management, structured outputs, verification and evaluation of non-deterministic behaviour.

· Experience implementing and comparing bounded loops, reflection or critique, planner-executor and evaluator-optimizer patterns against simpler baselines, with measurable treatment of quality, reliability, latency, token usage, cost and failure modes.

· Experience building agent or model harnesses across tool interfaces, context and state management, orchestration, policy enforcement, testing, tracing, replay, deployment and observability.

· Hands-on experience evaluating, selecting, training or adapting open-source SLMs or LLMs, including benchmarking, governed dataset preparation, supervised or parameter-efficient fine-tuning and inference optimisation.

· Practical ModelOps experience for trained or fine-tuned models, including dataset and model lineage, experiment and artifact tracking, registries, evaluation gates, controlled release, performance and drift monitoring, rollback, retraining and retirement.

· Experience designing model routing, batching and safe caching with appropriate keying, invalidation, freshness, privacy, access-control and provenance safeguards.

· Strong grounding in modern machine learning, experimentation and statistical evaluation, with disciplined handling of train-test separation, leakage, uncertainty, reproducibility and model comparison.

· Ability to write production-quality Python and work effectively across data preparation, model development, APIs, orchestration, testing, deployment and observability.

· Experience with unstructured or multimodal information, semantic retrieval, provenance and data-quality engineering in workflows where domain experts provide ground truth and structured feedback.

· Experience working directly with users or domain specialists to turn ambiguous needs into usable capabilities and iterate based on observed workflow outcomes.

· Sound judgment on model and architecture trade-offs across efficacy, efficiency, security, licensing, maintainability and operational risk.

· Clear communication a