AI Transformation Leader

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Job Title: Associate Distinguished Engineer - AI Transformation Leader

Duration: Full-time

Location: NYC

Mode: Onsite – Hybrid (1-2 times a week in office)

Job Description

Associate Distinguished Engineer – AI, Data Science & Agentic Solutions

As an Associate Distinguished Engineer – AI, Data Science & Agentic Solutions, you will act as a senior technical authority responsible for architecting, validating, and scaling next-generation AI systems across enterprises. This role is deeply hands-on with modern AI/ML ecosystems, agentic architectures, large-scale data platforms, and cloud-native engineering patterns.

You will partner with senior technology leaders to define end-to-end AI architectures, recommend engineering strategies, and shape the technical roadmap for deploying advanced AI systems such as LLMs, multimodal models, agentic pipelines, retrieval systems, and enterprise ML/LLMOps platforms.

Your focus is to guide solution direction, validate architectural decisions, and remove technical ambiguity, ensuring organizations adopt AI responsibly, securely, and at scale — while staying independent of daily execution cycles.

Key Responsibilities

• AI Architecture & Technical Leadership
• Architect enterprise-grade AI systems using LLMs, multimodal models, vector databases, knowledge graphs, and agentic orchestration frameworks.
• Design end-to-end pipelines including data ingestion → feature engineering → model training → evaluation → deployment → feedback loops.
• Define and enforce engineering standards for MLOps, LLMOps, data quality, model observability, guardrails, prompt security, and hallucination mitigation.
• Consult on scalable microservices, model serving layers, retrieval-augmented generation (RAG) pipelines, and autonomous agent workflows.
• Conduct architectural reviews, performance tuning, and technical due-diligence for high-risk or complex AI solutions.
• Advanced AI/ML Engineering
• Guide on how to build quick prototypes, PoCs, and production systems using modern AI stacks (transformer models, diffusion models, graph models, reinforcement learning, and agentic systems).
• Advise on selection of foundation models and fine tuning approaches.
• Advise on real-time data streams, event-driven systems, API layers, and cloud-native compute.
• Establish evaluation frameworks: bias, drift, explainability, reliability, performance.
• Lead complex troubleshooting, debugging, and optimization of AI pipelines and distributed training workloads.
• Data Platform & Infrastructure Architecture
• Architect secure, high-throughput data platforms for AI/BI use cases based on lakehouse, medallion, streaming, and vectorized storage patterns.
• Define data governance, metadata, lineage, cataloging, and policy enforcement mechanisms.
• Deploy scalable compute using Databricks, Snowflake, Kubernetes, Ray, SageMaker, Vertex AI, and Azure ML.
• Technical Advisory & Engineering Governance
• Guide CIO/CTO/CDO teams on AI system design, architecture modernization, model lifecycle governance, and platform engineering standards.
• Translate ambiguous requirements into well-scoped technical blueprints, reference architectures, and engineering backlogs.
• Evaluate enterprise readiness across data, models, infrastructure, and processes — producing AI maturity assessments and architectural recommendations.
• Mentor engineering teams in building reliable, secure, and scalable AI systems with measurable outcomes.
• Innovation & Ecosystem Leadership
• Lead deep-dive technical workshops on agentic systems, generative AI patterns, model safety architectures, continuous learning loops, and intelligent automation.
• Collaborate with hyperscalers and partners (AWS, Azure, Google Cloud Platform, Databricks, Snowflake, NVIDIA) on technical accelerators, performance benchmarks, and reference implementations.
• Stay ahead of emerging architectures (multi-agent, RAG 2.0, synthetic data generation, self-improving systems) and translate them into actionable engineering strategies.

Qualifications

• 12+ years in AI/ML, data engineering, or large-scale distributed systems.
• Deep hands-on expertise in:
• Foundation models (LLMs, multimodal, vision, speech, embeddings)
• Model finetuning, training, inference optimization, evaluation
• MLOps/LLMOps workflows and ML engineering best practices
• Vector databases, knowledge graphs, retrieval systems
• Strong experience with cloud-native architectures (AWS, Azure, Google Cloud Platform) and data platforms (Databricks, Snowflake, BigQuery, Lakehouse).
• Demonstrated ability to design complex AI systems that operate reliably at scale.
• Experience influencing senior technology leaders through architectural clarity and technical depth.
• Strong documentation, architecture storytelling, and ability to simplify complex technical concepts for varied audiences.
• Track record of publications, open-source contributions, patents, technical talks, or recognized technical leadership is a strong plus.