Associate, AI Data Scientist

Location: Bengaluru
Department: Data Science

We are seeking a hands-on applied AI practitioner to join our multidisciplinary AI and data science team. You will be responsible for researching, building, and scaling advanced agentic systems and fit-for-purpose language models that turn complex information into dependable data, research, and decision intelligence.

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.

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.
• Engineer advanced agent patterns, including bounded iterative loops, reflection and critique, planner-executor and evaluator-optimizer patterns.
• 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.
• 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.
• Develop model-adaptation strategies using prompt and context optimisation, supervised fine-tuning, parameter-efficient fine-tuning, or other suitable techniques.
• 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.

Requirements:

• Strong experience building and deploying AI or machine-learning systems in an enterprise, SaaS, data-product, or similarly complex environment.
• 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.
• 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.
• Practical ModelOps experience for trained or fine-tuned models.
• 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.
• 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.
• Understanding of private markets, alternative investments, financial data, or adjacent institutional-investment workflows is desirable.

Benefits:

• Strong retirement plan
• Tuition reimbursement
• Comprehensive healthcare
• Support for working parents
• Flexible Time Off (FTO)

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