Associate, AI Data Scientist- Private Markets

Overview

Join a multidisciplinary AI and data science team to research, build and scale enterprise-grade agentic AI capabilities and fit-for-purpose language models for private-markets intelligence, working directly with users and senior technical leaders.

What you'll do

• Work forward-deployed with researchers, data specialists and product teams to understand workflows, frame ambiguous problems, define measurable outcomes and iterate 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 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.
• 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.

What you'll need

• 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 and collaborative delivery skills, with the ability to explain experiments, limitations and production trade-offs to technical and non-technical audiences.
• Commitment to responsible AI, data provenance, privacy, security and meaningful human control in high-trust enterprise workflows.

Nice to have

• Understanding of private markets, alternative investments, financial data or adjacent institutional-investment workflows; curiosity and the ability to build domain depth quickly are essential.

Details

• Location: Bengaluru, India.
• Hybrid work model: at least 4 days in the office per week, with the flexibility to work from home 1 day a week.