New opportunity

Senior Associate, AI Solutions

Portage Point Partners · Gurgaon, India

About this role

The Senior Associate, AI Solutions develops and deploys AI tools for Portage Point Partners’ financial and operational advisory work. Working with the Vice President, AI Solutions and teams across the firm, the role turns consulting frameworks and business requirements into scalable AI-driven products. The position is hybrid in Gurgaon, India; candidates must be willing to relocate there or be primarily present there and collaborate across global time zones, including with U.S.-based teams.

Responsibilities

Develop AI-powered solutions, agentic workflows and automation tools for advisory work and operational excellence. Build, test and improve LLM integrations, prompt-engineering strategies and AI pipeline components for reliable, scalable outputs.
Use AI coding tools such as Claude Code to accelerate development and produce maintainable code. Work with data science, product and delivery teams on functional prototypes and production-ready solutions.
Support data pipelines, model evaluation and quality assurance against performance, reliability and compliance standards. Research emerging AI tools and techniques and recommend practical applications.
Document workflows, technical specifications and best practices; participate in training; and help prepare AI-related proposals, content and client presentations. Support talent acquisition and other firm-building initiatives.

Qualifications

A bachelor’s degree in Computer Science, Engineering, Mathematics, Data Science or a related technical field from a top undergraduate program, plus three to five years of experience in AI/ML development, software engineering or a related technical discipline.
Hands-on experience deploying LLM-powered applications, agentic workflows or AI automation; proficiency with AI coding tools such as Claude Code; and strong Python or JavaScript fundamentals. Familiarity with AI/ML frameworks, APIs and orchestration platforms is required, along with knowledge of data pipelines, structured and unstructured data processing, model evaluation, and responsible AI principles including bias, explainability and privacy.
Strong analytical, communication and executive-ready presentation skills; attention to detail, ownership and the ability to explain technical concepts to non-technical stakeholders and work in lean, fast-moving teams.

Preferred

Consulting or professional-services experience and experience with OpenAI, Claude, LangChain, n8n or similar tools.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Agentic AI workflowsLLM application developmentPrompt engineeringAI automationClaude CodePythonJavaScriptAI/ML frameworksAPIsAI orchestrationOpenAIClaudeLangChainn8nData pipelinesStructured data processingUnstructured data processingModel evaluationQuality assuranceResponsible AIBias detectionExplainabilityData privacyTechnical documentationAnalytical problem-solvingExecutive presentationsCross-functional collaboration

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Turn advisory methods into usable AI products
The role translates consulting frameworks and business requirements into prototypes and production-ready solutions for financial and operational advisory work. Prepare to explain how a workflow moves from an advisory use case to a deployable tool.
Build and improve agentic and LLM workflows
The posting emphasizes developing and deploying agentic workflows, automation tools, LLM integrations, prompts and AI pipeline components. Prepare to discuss implementation and iteration decisions, not just model selection.
Make outputs reliable and responsible
The role supports data pipelines, model evaluation and quality assurance against performance, reliability and compliance standards. The qualifications also cite bias, explainability and privacy. Prepare a concrete approach to testing and reviewing outputs.
Develop quickly without sacrificing maintainability
The posting calls for AI coding tools such as Claude Code to accelerate work while producing well-structured, maintainable code, backed by Python or JavaScript fundamentals. Prepare to distinguish tool-assisted speed from code review and engineering judgment.
Work across teams and communicate for adoption
The role collaborates with data science, product and delivery teams, documents workflows, contributes to training, and supports proposals and client presentations. It also requires collaboration with U.S.-based teams across time zones. Prepare to present technical decisions clearly to non-technical stakeholders.

Likely challenges to think through

Balancing reuse with advisory-specific needs
Preparation hypothesis: Turning consulting methodologies into scalable products may require preserving the details of each use case without building a one-off tool every time. Prepare an example approach that separates reusable components from case-specific inputs and review steps.
Balancing rapid iteration with dependable outputs
Preparation hypothesis: Fast LLM development may compete with the stated reliability, compliance and privacy expectations. Prepare to describe how evaluation criteria, representative test cases and human review could guide release decisions.
Keeping distributed stakeholders aligned
Preparation hypothesis: Lean teams, cross-practice collaboration and U.S.–India time zones may make timely decisions harder when technical and advisory teams need different levels of detail. Prepare a concise plan for documenting decisions, resolving ambiguity and presenting trade-offs to senior stakeholders.

Practice your answers

Technical practice

How would you design an agentic workflow for an advisory task?

What to coverThe role develops agentic workflows and automation tools for financial and operational advisory work. Suggested preparation: explain how you would define the task, control the workflow, check outputs and decide whether automation is appropriate.

How would you turn a consulting methodology into an AI prototype and then a production-ready solution?

What to coverThe posting calls for translating business requirements and consulting methodologies into prototypes and production-ready products. Suggested preparation: discuss how you would clarify requirements with product and delivery teams, validate a prototype and address reliability before deployment.

How would you test and improve an LLM integration and its prompts?

What to coverBuilding, testing and iterating on LLM integrations and prompt strategies is a stated responsibility. Suggested preparation: describe representative test inputs, how you would assess output quality and how you would distinguish a prompt issue from an integration issue.

How would you use an AI coding tool without compromising code quality?

What to coverThe role uses tools such as Claude Code to accelerate development while producing maintainable code. Suggested preparation: explain how you would review generated Python or JavaScript, test its behavior and keep the resulting code understandable to teammates.

How would you prepare structured and unstructured data for an AI pipeline?

What to coverThe posting specifies data pipelines and processing of both structured and unstructured data. Suggested preparation: discuss input validation, data quality and how processing choices affect the reliability of downstream outputs.

How would you evaluate whether an AI solution is ready for use?

What to coverModel evaluation and quality assurance against performance, reliability and compliance standards are part of the role. Suggested preparation: describe a test set, meaningful success measures and how you would investigate failures before recommending deployment.

How would you troubleshoot an AI workflow that produces inconsistent results?

What to coverThe role is expected to build and improve reliable, scalable AI pipeline components. Suggested preparation: explain how you would isolate problems in inputs, prompts, API responses and workflow steps, then verify that a fix works beyond one example.

How would you address privacy, bias and explainability in an advisory AI tool?

What to coverThe qualifications explicitly mention responsible AI principles, including bias detection, explainability and data privacy. Suggested preparation: discuss practical checks for sensitive data and uneven outputs, and how you would communicate an output’s limitations.

How would you compare AI APIs, frameworks or orchestration tools for a use case?

What to coverThe role involves AI/ML frameworks, APIs and orchestration platforms, plus research into emerging tools; named products are preferred examples rather than mandated choices. Suggested preparation: compare options against the task’s reliability, maintainability and integration needs, and explain what evidence would support a recommendation.

What would you document so another team could maintain an AI workflow?

What to coverDocumenting workflows, technical specifications and best practices is a stated responsibility. Suggested preparation: outline the design decisions, inputs, tests, limitations and operating instructions another developer would need.

Behavioural practice

When have you aligned technical and delivery teams around an ambiguous requirement?

What to coverThe role collaborates with data science, product and delivery teams to translate business needs into solutions. Suggested preparation: give a real situation, the actions you took to resolve differing interpretations and the resulting decision or deliverable.

When have you taken ownership of a quality issue under time pressure?

What to coverThe posting emphasizes attention to detail, responsiveness, ownership and work in high-pressure, fast-moving teams. Suggested preparation: describe the situation, how you prioritized and communicated your actions, and the result.

When have you explained a technical recommendation to a non-technical senior audience?

What to coverExecutive-ready presentations, client-facing content and clear explanations for non-technical stakeholders are relevant to this role. Suggested preparation: use a real example that covers the audience’s decision, how you framed trade-offs and the outcome.

When have you coordinated work across time zones?

What to coverThe Gurgaon-based role involves collaboration with U.S.-based teams across global time zones. Suggested preparation: describe a real situation, how you managed handoffs and expectations, and what resulted.

When have you helped teammates adopt a technical practice?

What to coverThe role includes documentation, internal training and best-practice development. Suggested preparation: explain the situation, what you shared or changed, and how you observed its effect on the team.

Sharpen your knowledge

Topics to prepare

Agentic workflow design
Posting: The role builds agentic workflows and turns consulting methodologies into scalable AI products. Suggested exercise: Sketch a hypothetical advisory workflow, identifying which steps need an agent, which can use simpler automation, and where human review belongs.
LLM reliability and prompt strategy
Posting: The role tests LLM integrations, prompts and AI pipeline components for reliable, scalable outputs. Suggested exercise: Compare two prompt approaches for the same sample task and explain how you would detect inconsistent or unsupported answers.
Data pipeline concepts
Posting: The qualifications specify structured and unstructured data processing and model evaluation. Suggested exercise: Diagram how sample tables and documents would be ingested, checked, transformed and passed to an LLM application.
Evaluation and responsible AI
Posting: Solutions must meet performance, reliability and compliance standards; bias detection, explainability and privacy are named qualifications. Suggested exercise: Draft evaluation criteria for a hypothetical advisory assistant, including failure cases and privacy checks.
Requirements-to-product translation
Posting: The role works with data science, product and delivery teams to turn business requirements into prototypes and production-ready solutions. Suggested exercise: Convert a fictional advisory request into inputs, outputs, acceptance criteria and open questions for stakeholders.

Skills to revise

Build an LLM application
Posting: Hands-on LLM application development, APIs and strong Python or JavaScript fundamentals are required. Suggested exercise: Implement a small application in Python or JavaScript that calls an LLM API, validates its response and handles errors.
Implement an agentic workflow
Posting: Building and deploying agentic workflows or AI automation is an explicit qualification. Suggested exercise: Prototype a workflow for a fictional advisory task with clearly defined steps, a review checkpoint and a documented failure path.
Use an AI coding tool critically
Posting: The role calls for proficiency with AI coding tools such as Claude Code and maintainable code. Suggested exercise: Use Claude Code to help refactor a small module, then review the changes, add tests and document what you corrected.
Prepare and test data inputs
Posting: The role supports data pipelines and structured and unstructured data processing. Suggested exercise: Write a small pipeline that processes a sample table and document, flags missing fields and records processing errors.
Run a quality-assurance review
Posting: Model evaluation and quality assurance support performance, reliability and compliance standards. Suggested exercise: Create a small set of representative and edge-case inputs, score outputs against defined criteria and summarize the failures.
Explain a prototype to stakeholders
Posting: Technical documentation, executive-ready presentations and explanations for non-technical stakeholders are required. Suggested exercise: Write a one-page technical specification and a short executive summary covering a prototype’s purpose, evidence of quality, limitations and decisions needed.

Your preparation checklist

Map the stated requirements to evidence
Prepare a short evidence sheet covering relevant education, three to five years of technical experience, deployed LLM or automation work, Python or JavaScript, and AI coding tools. Note any gaps honestly rather than inventing examples.
Build a project walkthrough
Choose a real LLM application or agentic workflow you worked on. Be ready to explain the business requirement, your contribution, architecture, deployment, iterations and measurable results, including what you would improve.
Practice an advisory workflow design
As a preparation exercise, sketch how an AI tool could turn a financial or operational advisory requirement into a prototype and then a production-ready solution. Show the data flow, LLM integration, human review points and maintainability choices; this is not a stated interview exercise.
Prepare an evaluation and risk plan
Draft a compact test plan for structured and unstructured inputs, output quality, reliability and compliance. Include how you would assess bias, explainability and data privacy, which the posting names as responsible-AI concerns.
Demonstrate your development approach
Prepare a concise example of maintainable Python or JavaScript code and explain how you used an AI coding tool, if applicable, while reviewing its output. Be ready to discuss API and orchestration choices without implying that a preferred tool is mandatory.
Rehearse an executive-ready explanation
Create a one-page, non-technical summary of a real technical decision: the problem, recommendation, evidence, limitations and business impact. Practice explaining how you coordinated with product, data science or delivery colleagues where your experience supports it.
Bring focused questions and confirm logistics
Ask how the AI Solutions team prioritizes advisory use cases, measures production quality and collaborates with U.S.-based teams. Be ready to discuss the posting’s Gurgaon presence or relocation requirement and cross-time-zone collaboration.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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