Associate Principal - Data Sciences
Listed on 2026-05-19
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IT/Tech
AI Engineer
Role Type Technical leadership architecture design handson implementation platform engineering through field collaboration
LTM is a global technology consulting and digital solutions company that enables enterprises across industries to reimagine how they operate, compete and grow. By combining deep domain knowledge, advanced engineering and transformation expertise, LTM helps clients navigate their most complex challenges and seize new opportunities in an increasingly digital world.
Blue Verse is LTMs Enterprise AI business unit purpose-built to help organizations move beyond pilots into scaled production‑grade AI adoption. Blue Verse blends AI strategy, functional consulting, an AI Foundry and agentic AI platforms to deliver integrated solutions that transform how work gets done across the enterprise.
Operating across Banking, Insurance, Manufacturing, Retail/CPG, Energy, Utilities, Travel, Hospitality and HiTech/Media, Blue Verse works closely with industry GTM teams to create repeatable AI solutions, accelerators and transformation blueprints.
Role OverviewThe Gen AI Architect is responsible for designing and operationalizing enterprise‑grade GenAI capabilities within Blue Verse. This role architects production‑ready AI solutions that combine LLM reasoning, retrieval, orchestration and enterprise controls to deliver trustworthy business outcomes in real operating environments.
You will work across architecture, product and engineering teams to move GenAI features from concept to robust implementation with emphasis on quality, safety, performance, maintainability and clear operational ownership. The role is both foundational and execution‑oriented, setting strong architectural patterns while helping teams implement them in ways that are observable, testable and resilient under production constraints.
You will collaborate with field and client‑facing teams to shape high‑value use cases, build rapid proofs‑of‑value, and convert successful pilots into production‑ready MVP plans that move from rapid proof‑of‑value to scalable enterprise rollout with strong governance, observability and measurable business impact.
Key Responsibilities GenAI Architecture and DesignDesign solution patterns for enterprise GenAI use cases including copilots, autonomous task agents, decision‑support systems and workflow automation across multiple business domains.
Define architecture for model routing, prompt strategy, grounding, memory handling and tool orchestration with explicit design choices for reliability and cost control.
Establish patterns for secure integration of enterprise data APIs and workflow systems into GenAI applications without compromising governance and data boundaries.
Ensure designs support explainability, policy enforcement and operational observability so teams can diagnose and improve behavior continuously.
Productionization and QualityBuild and guide implementation of evaluation frameworks for accuracy, relevance, safety, hallucination control, latency and consistency across release cycles.
Partner with platform engineering to standardize reusable components for prompt templates, guardrails, telemetry, fallback logic and controlled rollout mechanisms.
Create practical evaluation harnesses for task success, policy compliance, tool‑call reliability and cost‑latency trade‑offs to compare baseline versus PoV improvements.
Contribute to performance and cost optimization across model and infrastructure choices through benchmark‑driven architecture decisions.
Create reference implementations that can be reused across domains and client deployments, reducing setup time and implementation risk.
Field‑to‑Platform ExecutionPartner with client‑facing and delivery teams to translate business problems into measurable outcome hypotheses and technically feasible, thin‑slice PoV scopes.
Define agentic blueprints covering agent roles, orchestration patterns, knowledge strategy, guardrails, observability and human‑in‑the‑loop escalation.
Produce clean handoffs to delivery squads with MVP backlogs, architecture decisions and production controls such as identity, audit logging and fallback design.
Design for enterprise…
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