Forward-Deployed Product & AI Engineer

MathCo; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

MathCo, a global Enterprise AI and Analytics company, seeks a seasoned forward-deployed product and AI engineer to anchor the offshore seat of a small, integrated global delivery team. You will own the application and intelligence layer of AI-enabled decision solutions built on the client's enterprise platform.

You will design end-to-end architectures for AI-enabled decision applications, manage platform-native builds on Databricks/Snowflake and cloud ecosystems, and drive production-grade

Qualifications

• 8+ years of software and AI engineering experience.
• Analytics experience and consulting background.
• AI Engineering Professional certification required (Databricks).

Responsibilities

• Design end-to-end architectures for AI-enabled decision applications and data foundations.
• Translate requirements into structured feature backlogs with clear acceptance criteria.
• Lead platform-native builds on Databricks/Snowflake across cloud ecosystems.
• Establish CI/CD, evals, secrets hygiene, and observability from day one.
• Deliver reusable assets, learnings, and playbooks at closure.

Skills

Software engineering

AI engineering

Consulting

Education

AI Engineering Certification

Databricks Training

Tools

Databricks

GCP

Azure

AWS

Snowflake

Job description

TheMathCompany or MathCo® is a global Enterprise AI and Analytics company trusted by leading Fortune 500 and Global 2000 enterprises for data-driven decision making. Founded in 2016, MathCo builds custom AI and advanced analytics solutions to solve enterprise challenges through its hybrid model. NucliOS, MathCo's proprietary platform, enables connected intelligence at a lower total cost of ownership (TCO).

At MathCo, we foster an open, transparent, and collaborative culture, making it a great place to work. We provide exciting growth opportunities and value capabilities and attitude over experience, enabling our Mathemagicians to 'Leave a Mark'.

We are looking for a seasoned forward-deployed product and AI engineer to anchor the offshore

seat of a small, integrated global delivery team. You will own the application and intelligence layer

of AI-enabled decision solutions - decision applications, agents, evaluation harnesses, and platform-native application surfaces - built directly on the client's enterprise platform, and you will use AI coding agents to deliver at a pace and quality bar conventional teams do not reach. The

role combines hands-on platform engineering, system design for LLM-inclusive architectures, and

the techno-functional fluency to carry business stakeholders through technical trade-offs.

Responsibilities:

System & Solution Design

• Design end-to-end solution architectures for AI-enabled decision applications and data foundations - ingestion, curated and gold layers, semantic models, and the application surface - on the client's enterprise platform.
• Design complex software systems in which LLMs are one component among many: retrieval and context pipelines, agent orchestration, evaluation harnesses, guardrails, and integration with enterprise systems - making deliberate architecture choices for accuracy, latency, cost, and failure modes, not assembling from a single vendor toolkit.
• Record significant design decisions as Architecture Decision Records (ADRs), capturing the rationale, the trade-offs considered, and the production consequence of each choice.

Techno-Functional Translation

• Act as the technical counterpart to business stakeholders: explain technology trade-offs - platform-native vs. custom, accuracy vs. cost and latency, scope vs. timeline - in business terms, and guide clients to informed decisions.
• Translate business requirements into a structured feature backlog with acceptance criteria and measurable evaluation thresholds; surface ambiguity and conflicting stakeholder aims early rather than absorbing them into scope.
• Apply working domain knowledge (CPG, Retail, Pharma, or Manufacturing) to data modeling, KPI definitions, and edge-case identification, in partnership with the onshore domain lead.

Platform-First Build

• Default to platform-native capabilities - on Databricks, Snowflake, or the relevant hyperscaler stack - for the front end, back end, and operational surface, reserving custom build for where it demonstrably earns its keep.
• Capture context gathered during the engagement in platform-native constructs (semantic layers, metric views, governed data products) so it compounds across markets and adjacent use cases rather than being rebuilt.
• Own data and access readiness at engagement start: profiling, data-quality gates, and the pipeline foundations the build depends on.

AI-Accelerated Delivery to a Production Bar

• Multiply personal throughput with AI coding agents: frame and direct multiple parallel build tracks, then specify, review, and harden what the agents produce - the quality bar is production engineering, not prototype output.
• Build production-ready from day one at the scope the engagement allows: CI/CD, versioned prompts and evals, secrets hygiene, and observability from the first sprint.
• Stand up the evaluation harness early and publish scores on a weekly cadence; demonstrate working software to the client from a correctly configured environment each week.

Delivery Discipline & Reuse

• Track delivery against committed timelines; make scope additions visible as priced scope trades rather than silent absorption.
• Run the engagement's quality gates and checklists through to sign-off; complete handover documentation - runbooks, ADR logs, operational artifacts - executable by client teams independently.
• Return reusable assets, learnings, and failure-register entries to the central library at closure, so each engagement hardens the next.

Required qualifications & experience

• 8+ years' experience in software and AI engineering, spanning application development and
• analytics, with 10+ years of overall consulting experience.
• Completed an AI Engineering Professional certification and required classes - e.g., Databricks
• Generative AI Engineer, Google Cloud Professional Machine Learning Engineer, or Azure AI Engineer.
• Professional-level certification on at least one MathCo-preferred platform - Google Cloud (including Gemini), Databricks, AWS, Azure, or Snowflake; professional tier preferred over foundational / associate.
• Minimum 6-8+ AI application projects delivered with hands-on development experience on Databricks or other MathCo-preferred platforms.
• Working knowledge of two or more common cloud ecosystems (AWS, Azure, GCP), with deep expertise in at least one.
• Deep experience building and shipping LLM-based applications - retrieval pipelines, agent orchestration, and evaluation harnesses - with working knowledge of Spark and distributed data processing.
• Familiarity with CI/CD for production deployments.
• Working knowledge of LLMOps and MLOps.
• Current knowledge across the breadth of Databricks product and platform features, particularly its generative AI and agent tooling.
• Familiarity with optimizations for performance and scalability.
• Demonstrated experience designing complex software systems, including current experience designing systems that incorporate LLMs - as distinct from building solely with managed toolkits such as Vertex AI or Azure AI Foundry.
• Domain exposure in one or more of CPG, Retail, Pharma, or Manufacturing preferred.